{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercícios\n",
    "\n",
    "1 - Aplique os algoritmos K-means [1] e AgglomerativeClustering [2] em qualquer dataset que você desejar (recomendação: iris). Compare os resultados utilizando métricas de avaliação de clusteres (completeness e homogeneity, por exemplo) [3].\n",
    "\n",
    "* [1] http://scikit-learn.org/stable/modules/clustering.html#k-means\n",
    "\n",
    "* [2] http://scikit-learn.org/0.17/modules/clustering.html#hierarchical-clustering\n",
    "\n",
    "* [3] http://scikit-learn.org/stable/modules/clustering.html#clustering-evaluation\n",
    "\n",
    "2 - Qual o valor de K (número de clusteres) você escolheu para a questão anterior? Desenvolva o Método do Cotovelo (não utilizar lib!) e descubra o K mais adequado. Após descobrir, aplique novamente o K-means com o K adequado. \n",
    "\n",
    "* Ajuda: atributos do [k-means](http://scikit-learn.org/0.17/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans)\n",
    "\n",
    "3 - Após a questão 2, você aplicou o algoritmo com K apropriado. Refaça o cálculo das métricas de acordo com os resultados de clusters obtidos com a questão anterior e verifique se o resultado melhorou.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aluno: Marcos Felipe de Menezes Mota - 354080"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from sklearn import datasets\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn.cluster import KMeans\n",
    "from scipy.spatial.distance import cdist\n",
    "from sklearn import metrics\n",
    "from sklearn.decomposition import PCA\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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aTQVQSqFyHwbPRBALICAuaPoBYu2aaPHqFG32qQS9Tz0Mw2IxvWZ32uh9cs86lkij0VQK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\nQZb8jTEmBVnyN8aYFGTJ3xhjUtD/AtpfFPWZ9BFEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa1f18a8208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = datasets.load_iris()\n",
    "X = dataset.data\n",
    "y = dataset.target\n",
    "\n",
    "data2d = PCA(n_components=2).fit_transform(X)\n",
    "data2d_df = pd.DataFrame(data2d)\n",
    "data2d_df[2] = y\n",
    "plt.scatter(data2d_df[0], data2d_df[1], c=data2d_df[2])\n",
    "plt.title(\"Plot dos valores reais\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=3, random_state=0).fit(X)\n",
    "ypred_kmeans = kmeans.predict(X)\n",
    "\n",
    "ypred_agg = AgglomerativeClustering(n_clusters=3, affinity=\"euclidean\").fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metricas homogeneity, completeness e v measure respectivamente para o Kmeans, k=3 \n",
      " (0.75148540219883386, 0.76498615144898163, 0.75817568000577862)\n"
     ]
    }
   ],
   "source": [
    "metrics_kmeans = metrics.homogeneity_completeness_v_measure(y, ypred_kmeans)\n",
    "print(\"Metricas homogeneity, completeness e v measure respectivamente para o Kmeans, k=3 \\n\", metrics_kmeans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metricas homogeneity, completeness e v measure respectivamente para o Cluster Aglomerativo \n",
      " (0.76080084697187234, 0.77959580055911437, 0.77008366164878694)\n"
     ]
    }
   ],
   "source": [
    "metrics_agg = metrics.homogeneity_completeness_v_measure(y, ypred_agg)\n",
    "print(\"Metricas homogeneity, completeness e v measure respectivamente para o Cluster Aglomerativo \\n\", metrics_agg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zufeoo0l1OItiAeKlgXbabLx6yum8esrpjOzWvVzpojWakmS2asxBA7phd0Sv\npJNSXJxx48k1em5JHUdsmgUnuIYiRhNU/nhivXc8UPgRKrIBqpRCBTegglnFunxuMS4Avu9Ryvzd\nqdAWwjn3Et5xHOFd/0L5ZkT1VsofWfFbZP/0TgbPh6jce1E7Ty2SpaGgV/5lEFaKB3/5kQ+X/Ynd\nMPAGgzRNTuaELl1Zn2Mdcu+wGbRMTWPGxZcyecXfbCvIJ9nh4IU/fo9KBOe227nk8L44LTKDaho2\nc6Yu4K37PmTL2u207d6KC+87i74nHFbqmP98dBP3jPov65ZlYXfYCPgCnH7jKI46c2CNyiquYai0\nWyH/afOACoDrKCT9CfN10NqzDbFDaDMq5EXtuQFCOwGFsrWLrOytc2eZnjRhVGgzaucpkf2GIITW\no7L/RKXdhpFyfqRrKZ43e1GF5iZ1wSQk7YYKXv3+i9TV5GN9+/ZV8+bNS7QYvDzvD57/I34SNytS\nHA7mXXYVrhIRwNNXr+SRX38hKzeHNJeLy3v34/K+/fVqXxPFTx/O4ulLXsRXGL1Z2r1vFx6ecgeN\nW5Ru716/PItdm7PpenhHSzfPmkIpHwQ3gC0TKeaSGc6+2kzTEHMDcEHTb2DXyWYgVBECkgGOfuD/\ngej9BAFHb4zM9wnn3AueT4i5SUgK0nwOIk7T3r99kFnspSxs7TCaJa4SWHUhIvOVUn3L6qdX/mXw\n2qL5FVL8brudu44cFqP4AY7v0o3ju3QjEAphj2wKazTFUUox4Za3YhQ/mD78tw5/gIlLnin1u9Ph\noLZ0OKhtTYppiYgLHN1ij6dejfLNJDrnvRuSz0Z8P1l43yjAD85+4P+12DgDSEbSHzZf+mcT9+kg\nuBYcByBiQ6XdArkPY5nPKApXGe31C23zL4NcX/mSszkMg+GduvDaKadz7sGHlt7XZtOKX2OJt9BH\n9rY4q1QF29fv5K/Z/wCwdukGPnj8Cz577mt2btpVi1JWDHH0QJq8CvaegAHSGFKvRNLuQIW2YqmU\nlR88HxFtslEgxr4UyvFiFFQAjH2BcEbyWUjGU2A/MJIBNIXYrDRJkHxuZS9xv0Sv/EshrBQd0zP4\nZ3fZPyyHzcaEk0+tBak09RmX24nL7aIwr2RlqAgibFu/g1lf/MHk8d8SDISw2Q1evfNdbn7tKo45\n54hKn1spHwSWgLjAfjAi1bc2FGdfpKlFvIGzN8qTbFEQxYDQeqLTJChQPlThe2DvAkELDyOc4BoS\n408vSccjScebswQ3oHafH8m7E3nqcB2FaOWvATMg66IvPmFDbvnyqHdvUneCNzT7L4ZhcObNJ/Pe\no58R9MeaNELBEGIIk1+chs9PxnoLAAAgAElEQVTjLzoG8PQlL9HvhMNIa1xx186w51vIvRNzRRw2\nV8iNJyCOA6pyOWXjGga2LpFN4b1P2Ulg72S6j8YkQvOB5xtUKIvY0ol2U/GnP13qKcXeHpr9BP6Z\nENoGjl6I48BquZyqosL5EFwNthZINURfl4ZW/nH4aNmfLNm2FW857P0CPHLMcTUvlKZBcP49Z+At\n8PHRU19G7ZG6kp0MOrkvy2Yux++J3ROw2QzmfrOQY86LzqmzdukGvn97Bj6PjyNOG0CvYT2jzI4q\nuBZybiPK/KIKUbsuQDV+FgltA8dBiKP6UyuI2CDzHVTBG+D5ErBB8hhzs3e31UrcafFEEMFxOEbj\nV8p5Xrt546kjKKXMSOn8CaYXlPKjnIOQjGcRo2biNLTyj8Nny5dZbvQm2e10TM9g1e5dhJQq2ri9\nYdpUbh18BMM7d02AtJr6hGEYXPb4vzj12hN5/e73mPfdEtwpLk6+8gROu24kj/3rubhpEkoe/uKF\nqUy6/V0C/iAqHGba6z8x5LQB3P7mNUU3AOX5GOuN0xzIvgKFASiUsz/S+MVqz5sv4kZSr4TUK6OO\nhx2HQGAxUYpe7KDibfKurFa5ahXv15A/EfDuu+H7Z6Ny7kQaP18jp9QbvnGwGdZvjSHC0yeM5MWT\nTsFltxMIh/GHQqzcvYvrvv2aqStX1LKkmvpKs7aZ3PbmtXy0eSJvrnyBU689kQfOfIpZX1q7QIeC\nYfqP3JdCIHvbHibe9g4+j59wKIxS4C3wMevzOSz8sVhq5PAurJW/wjTFeAAv+P9A5ZdvZV0aSvlQ\n3mmows9Qoc1x+0njV8B9EuAEDLAfAo3fxMwfaYHNqobU/oEqmESsGcsPvp9Q4dwaOadW/nE45+BD\ncdtjy65lJCVxYGZT/jtzRoxJyBsM8visX2tLRE0D48sXvmHB938S8AWijosIziQHN796RZS9f960\nxZYZQL0FPn79ZPa+8c6hxZKQlYY34oFTeZR/MWr7YLPCVd4DqB0nEM57xrKvGKkY6Y8jLRYjLZZg\nNP0Uw9kLks/DsmZu6rVVki2hhOM4lYgNwtVXv7k4WvnH4bQDe3BMp8647XacNhspDgeNXC5ePmk0\nIsL6OAXcN+bmFD2ST1+9kuFvv86B4/+P4W+9xrTV+/FjqaZGmTN1AVf3u53TMsdy09B7WTorNvHt\n1xO/j6nuBWDYhOdmPcwx50bb+h0uO1i4FBuG4HQXM90kHQf2A4hN0WCBKt31WQU3orzTzWpWJdtU\nEJU9DlRepLqVB/BBwZsoX/wiJyK2KFOTpN0CyReCuAEnSBNodD+SdEzZ8tdVnIOwVsdJNVZaU9v8\n42CI8PyJo1i2fRtzNmWRmZzM8Z274naYTwMtUlLZkh+buK1Zcgoiwrcr/+Gm774pejpYsyebG6dN\n5cnhIzipew17UGj2K0pG9P7563LuOP4hHv3mbg49qkdRv4DP2tZtc9hJs4jk7T+yNyocm9rA4XJw\n3AVDi16LOKDJ26jCz8A7xXwKCK6C8KYSI+2QNNxSBqVCqJzbwTsNxAGEULYuSJPX9mXg9M/DusKV\nB+X5CBBU3mOm7d7IhJQrkeRzY2JiRGxIo1tQadeb7pqSXq1uqYlAUq9F+X7cl6oCAVzQ6L64mU+r\nyv79jtUCPZu34N+H92H0AQcVKf412btxWeTjcdvtXDfALNbw2G/WZqEnftNmIc0+4kX0+jx+Jtz2\ndtSxYWcPxuGKNUVmtmpsWd0rOc3NvZ/cQlKyC3daEkkpLhwuBxc+cDZdD+8U1VfEiZFyDkbmuxhN\nJiKNx5v59IuiXt1gNEVSb7K+joI3wTudfXVrPRBcgcq5o/hVYV3yGwhtQWVfbhZsJ2hWGMt7HFUw\nwbo/5k1LjMb7veIHEHs7pOlX4D4X7N3BdTTS5A2MGswKqlf+FSTP5+PMj98nxxsdlWiIcMugIzgv\nEt27MU7St6yIWUhH+DZscnfn8d1bv7D2z43s2pJt2Wfd0g1Rr8+54zR++3Iu2zfuwpvvxZnkwGa3\nccc718X9PvU74TA+3DKR37+aj9/jp++Iw2jauuxSiOLoAU2/Mz2BgmvBcTjiPgUxUqwHeN4hNlI3\nAL5fUeECc5yjbxxPHXckAVvJ8R4oeBmVcnG1exjVRcTWEkkvJcV1NVPvlP93q1fx9O8z2ZSbS+fG\nTbhtyJEMqcbKWFP++RtfMBiToirJZqN9Rgb+UIjJ//yNy2bHG4r9ojdPSdWKv4GzdukGbjzyPwT9\nwaJALStKKumURsm8tOBJZn76O0tmLKdl5+accNGwMhO9Jae5Oebcikf+iq2p6YJZHsIlI3SL4wNS\nECMFlXwZFI5nX9oGNzh7QyBOWW8VgvDuaik3qYmmXin/ySuWc+cP04v88//cvo3LpnzBK6NGc2T7\njtVyjjXZ2Zb+/8GwYvXu3Tw9exbr9+yxVPxuu50bBgyuFjk0+y9PXjyegpzSlKVZr/df946JOe50\nOTjmvCNjArkSjmsoeL8gJnOnrbWZywcIF06GwonF+hggTkh/AvZcC4HdsfOK7B9F2/dD9n9jWQSl\nFI/NmhGjmL3BII/NnBFnVPn5PWsjoz94h7eWLLS0WtptBlm5uazbk40nGIhpz3Qnc89RR3NGj54s\n3rqFv3ZsL1c9U039oiC3kDWL18dtdyY5SM1I4dLHzmP4v46qRckqT7jgbfB+SbTit5lVstIfRURQ\nKgB5D2Cadvb2C5sbnIWvI6k3EOu+6YbkhmHySQT1ZuXvD4XYXlBg2bYm22JFUQHmbs7i35M/i5vq\nwWmz0bVJJst2bLPsk+Jw8OLIk/EGg/Sf9BLBUJgwinRXEhNPPpUezZpXST7N/oNhM+LueTZumc4r\nC5+iUWaapX9+XSTsnwt5D1m0KGjyGeLoYr4Mrsa6qEoAvD8habdBxnOovEfMur+SDimXISmX1qD0\nDZt6s/J32mykOa3zcbdILX9uDF8wyMacHDyBfav3J3+bGVfxZ7iSOP+QXrx72hjSXNbnV8p8Arni\n6y/Z4/WSH/BTGAiwJT+P8z/7CK/Fk4KmfuJOSeLwow/GZo/+6TmTHIy4+Bgat8iokOKv7adHpfxm\noFZwlXnu3P/G6RmGwIJ9L430+GkZIimaJelojGbfIy2WY7SYi5E6rl548tRV6s07KyJc1a8/7hJF\nVMprZ1dKMX7u7/SZ+CInvvsmfSa+yMMzfiYUDrNyl3X0nctmY/oFF/Ofo44mxenkgkMPi4kKFqB5\nSgpLtm0hZPFDDYbD/Lh2TfkvVLPfc8vrV9OiQzPcaUm43E6SUlwcOKAb599zRrnnmPHJbC7ocjXH\n28/i7Dbj+GrCdzV+Iwh7pqC2D0BlX4zaeQZq5ygIZcUfUCzXjthageNgYo0NbiTl4qgjNeXXromm\n3ph9AC49vC/hsOLFeX/gDQZIdbq4edAQTj2wR5ljP1j2Jy/OnRO1Z/D+0sWkOp20S08nZ3tswQmb\nYZBebLV/TMfOjD3scF5dOB+nYUMBaS4nk045jbeXLMIfKlmxyFT+uz1xcrdr6iWZrRrz2t/PsfCH\npWxdu50uh3XkwP5dy+0F9tvkuTwx9oWi2IDdW7J5+aY3CQdDnHLViBqRWQWWQ87dRLljhlZj5t2J\ng+vYqJeS8YIZ4RtcaQaCKT+kjkOSdEbcRFAva/iGwmEKAgFSnc5y18c98vWJbMqLTaCU6nDy3IiT\nuPqbKVGmH7fdzmW9+3HDwNiniu0F+czbvJkmbjf927TFEOGHNau5ftrXFAaiTTxJdjtfnH0+3TNj\ng3Q0GisuPfhG1v8Vu+JOb5rGx9terRFX4nDO3eD5lFi7fRKmK2cJPSKNkWY/WKYjVsFVENoBjp6I\n0ajaZW3olLeGb70x+xTHZhg0crkqVBh9Z6H1ZnFhMMDgdu15YvgIWqamYoiQ5nRxVb8BXDdgkOWj\ndvOUVEZ2687Atu2KZBjWsRM9mzWPMksl2x2c1LW7VvyaCrFlzTbL43nZBaXGDVSJ0HYsN2zFhmVO\nILUHtX0Iyj83doi9K+IapBV/gqlXZp+q0KNZcxZu3RJzvHVaGk6bjVHdD+Ckbt3xh0I4bTa++mcF\nQ9+YxKa8XFqkpHLTwMGM6XlI3PlthsHbp43h42V/8tnff+G02Tj34EMZ1b1uVBDS1Dy/TZ7Lh098\nye4t2fQefijn33MGzdtV/MbfuktL1i2LLWGY1iQVl7uG3CJdw8A/h5goXOXDWo0owIPKvgKaz9bu\nmnWQemn2qQzzt2ziws8/ibL5J9nt/G/ESTEFWqauXMEt330bYwa6b+gxnFXKDUDTcPnkmSm8ee+H\neCNZOW12G8mN3ExY/BRN21SsBOjvX83n4bOfiVrlu5JdXPHMRYwaVzP2c6U8qJ2nQmgz+8otusHe\nMZKPJw6SgmT8D3HVsaC0ekytmn1EZISIrBCRVSJyh0W7S0Q+jLTPEZGO1XHe6qRPqza8f8bZDO3Q\nkeYpKQxo05bXTjndsjKXleunJxjk6dmzaktczX6Et9DHG8UUP5h1dwvzPHzw+BcVnm/gqD7c9d4N\ntOnWCjGEZm0zufaFS2pM8UOk2lbmp5B6DdgPBudgJONZcJ9B6amgJb6LZyVRwbWE818inDceFdBp\n0itLlc0+YvpljQeOA7KAuSIyWSlVPKH3JUC2UqqriJwDPA6cXdVzVzeHtmjJ66PLdrez2hgG2FFY\nQCgctqwCFlaKHK+XFKcTp0VGUE39ZePfm2L8+gFCgRCLilfUqgCDR/dj8Oh+VRWtQoiRiqReDqmX\n7zsYzkPljwdVPHK3GCoIzv7VJkO44HXIewYIAQpV8AoqdRxG6jXVdo6GQnWs/PsDq5RSa5RSfuAD\nYHSJPqOBNyP//gQ4Vvbj7GZtG1lvVDVPSbFU/FNW/M3AV19m4KuvcNgrL/DIjJ8JWuRZ19RPGrdI\nJ+C3Xv02s0jF7C308drd73FO23Gc1foyXrrpDQpyrB0SEo0YaUjmx+AYVKLFDiRB+qPxM4HGQakw\nyr8I5ZuFCu+7bhXcGFH8Psyc9yHAC/kT9BNAJaiODd82QPHdpyxgQLw+SqmgiOQAmcDO4p1EZBww\nDqB9+/bVIFrNcNugI6MKtYBp87954JCYvr+uX8ftP0wr6hsIw7tLFxMIh7h/2LEx/TX1j6ZtMjls\naE8W/bw0qiCLK9nJObedGtVXKcVtwx9g1aJ1BLymW/CUl6Yxf/piXl74JHZH3fPREHt7JPMNwuEQ\n+H8H/88gjRD3aMResd+xCqxEZV9iVvuKmIxUo3sxks8E349YPl0QQHmnI45u1XA1DYfqWPlbreBL\nfkLl6YNSaoJSqq9Sqm+zZs2qQbSaYUS37jx13AjaN0rHEKF1WhoPHT3c0tvnuTmzLYu6fLhsaYzP\nv2b/Yufm3Xz1yndMnfQD2dtLr7N694c30ue4XjhcDtypSaSkJ3PN85fQa1jPqH6LflrK2qUbixQ/\nmBW8tm/Yyewp82vkOqoLw7BhJA3BaHQ3Rtq1FVf8KoTKHgvhrZEyj/mAF3IfjJSFFKxVieio4EpQ\nHcuILKBdsddtgc1x+mSJiB1IB6qWba2G2ZiTw8rdu+iYkUHnxrEpZUd2O4CR3coux5iVa60UDBGy\nPR6SHbGVmTR1ny/Hf8OEW99GDEFEGH/dq9w48QqGn2+diTOlUTIPTb6DPTtyyNmZR5uuLS1X8asW\nrI0p0A7gyffyz/zVHHl69EN1OBxm2/odJKe5SW+6n/vN+/+IlDGMaUAVfoCkXgV5T1q02yDphJqW\nrt5RHcp/LtBNRDoBm4BzgPNK9JkMXATMBs4EflR11Mc0EApx47Sp/LB2NU6bjUA4TN9WbXh51OhK\nKeqDm7fgp3VrYh5zbIbQLKVitlBN3SBr5RYm3PoOfm+0kn72spfpfewhNGnZOO7YjGbpZDRLj9ve\nomMznEkOPIHoVCBJKS5adWoRdWzutwt5+pKXyM8pIBwKc8iRPbjz3etKnb9Oo3KxXtmHIbwLsbVE\nNfoP5D4U6Rf5VaXdjNg7WYzTlEaVzT5KqSBwDTANWA58pJRaJiIPisgpkW6vApkisgq4CYhxB60r\nPP/HbH5cuwZfKESe3483GGTu5iwe+uUnAD5YuoTj336dk99/m9kbN5QxG9w0aAhJcZLNaa+f/ZNf\nPvqNUDA2T5MYwqzP/2DLmm08dcmLjD3gWm4/4SEW/VR+j55Bp/TFnerGMPYpQRGz6Pqws/elEln/\n10YeOPNpdm3JxlfoJ+ALsuSXZdx14qNVu7hE4ugLysIUKu6i/D9G8llIs++QtFvNv2bfYqSMrV05\n6wk6yKsEfSa8SLY3NtGa02ajcVIS20rUDDimQycmjT691Dn/3L6Nx2fOYOn2bTRPSeGa/gM55YCD\nqlVuTe3x1v0f8u7DnxIOR/92nEkOzrz5FL54fireAh/hkOnR5Up2csMrl8c1CZVk67rtPH7B8/z9\nx0oQofOhHbj9rWtJTksi4AvSslNznrtqIt9M+qHoHHtJSnHxfzMfpkuvjvz9x0o+enIyW9Zso9ew\nnpx588nlqt+bSML5L0L+K8De36Ab7F2RzPd1lHA5KW+Ql1b+xcjz+eg36SXL7Jul8fW5F3BQpCCL\nNxggFFakOPUXtb6yatFabhhyT0weHWeSg8OHH8LcqQtjbgyNMlP5aOskbBV42svfU4BSivw9BTx0\n1jOsW7YRwxDSmzYio3k6/8xbHTMmJd3Nne9cj8/j54mxL+D3+FEK7E4b7lQ3Ly94gubt664zBYDy\nzUYVvmd6/LhORJJPRcS6VoYmlvIq/7rnN5YAlFI88duvvLFoAaE4/vcG1nWIAB6d+QvPHD+S276f\nxqyN60FBz+bNeWL4CLplVix0X1P36XpYJ0ZfM4Ivx3+L3xtARHA47Vxw3xg+/9/UGMUP4PP42Zm1\nmxYdyq94UzNSCAVDjOt1M7s2Z6Mi827fsJNdW7JxJjli9h383iCde3Xkyj63FaV8Bgj6QxTkFPLW\nAx9z4yuX8/07M/ju7V9wOB2ceMkxHHnGwBrJBloZxDUIcZWMG9BUN1r5A28vWcRbixfis1jxOwwD\nh82GgZAfsM6YGFaKsz75gKzcnKKCLUu2bWXMJ+/zy0WXkp5UsjapZn/nsscvYOhZg5nxye/Y7AbD\nzh5Cp4Pb8/OHv7Frc3ZM/3BIkdq49A3+bet3sGLuKjJbN6HHoO6ICPO/W0JBjqdI8e9FRLA5bNhC\nYUKRzWFnkpMjTu9PwB/AWxBbfyIcCjNv2iLuGfVfls76G2+BmW5i6czlzJu2mJsmXlHZt0OzH6KV\nPzBhwdyYwu9g+hOcf0gvxh7Wm7t+mM5vWbGZFAGGd+rCM7/PiqrUpTDrCn/+91+MPax3DUmuSSTd\n+3She58uUcfOueM0nrx4PL5ieXycSQ6OOH0AKY2SLecJh8M8d+VEvnvrFxxOO0opMts04cnv72Xn\npt0xdn2AoD/IUWcOJDUjhZmf/0He7nzCoRC/fTGXxT8vIxiwNl06k5xRih/AW+Djh/d+5fQbTqJj\nz3aW4zT1D638gWxP7CoJTF/8WwYfyd0/fse8Tdbl6g5t3gKHzWZZotEbDFa5eLxm/2LomEFsXbed\ndx78GMMwCPiDDBjVhxsnxF9VT3/jZ35871cCvkCRj//mVVu57biHsDtsUTeSvSSluuh/Ym+GnjWI\nmZ/NIegPopQiGAhFJZCLGpPsot0Bra3rASjFoh+XVln5q+AqVO6j4J8HRhokX4CkXKaDsOogWvkD\nh7VsyWyLVX2bRulsyctl2qqV+C2Uux1YtmM7K361ztWT7HBwaIuWNSGypg5z9q2jOfWaEWxetZXG\nLTPK9Lv/4vlvolbiYJpoNv69ybK/M8lB83ZNOfLMgcz9dhGeAl/Z9XsFTr/xJJRSLPh+ScyTgc1u\no1FmbNWtiqBCm1C7xkQCtRSEvZD/Iiq0HkmPV+hdkyi08gfuPnIYYz7+AH8oSEgpBNO184Ghx7Bg\n65a4G2FBAKUIWewV2A2DxkluRnUvOwpYs3/h9/qZ9vpP/PLxbFLSkxl1xfH0O+GwqD4ut4tOh3Qo\n13yFeeWv4ZyUksQZN57EmFtOwelysHtLNuFyeKclpbhYuWANC39camkSMgyDQRXIEqoCf0NwDdi7\nFeXUUQWvmXV5o0IaveCZgkq9EcLbUAWvQmAjOA8C99kYTl3/IlFo5Y9ZxWvyOeczfu4c5mzaSJ7P\nT37AzzXfTKFDegbBCrh+GiKkOhyc0LU7tw4+kiS7Tt9Qnwj4A9x41L2s/2tjkTfN/O+WMObmk7no\ngcplKT/i9AF88fzUqKRv8UhrksLYB88pet1j8AHWuc5Kyu0NsvD7pQQD0eewO2ykNUnlwS9vx51S\ntmOCChegsi+HwBKzhKMKoZx9kcYvmsewCtJyoQo+hMKJFFUC8/wJno8I27oijV+ucB4gTdWplzV8\nK0OXJplccngf9ni9RV49BYEAf+3cQUCVP/2yTYSfx17K48NPoGmy9QafZv/ll49ms2F5VpQbpa/Q\nZ5Zn3Brr5VMezrnjVDJbNcGVbPqy2xzx7eMlyz52Org9A0b1ISm5dD/4UCgUo/jB9Bp6d8PLHNi/\nfBkxVd5/IbAI8JrJ1/CCfy4q72mwdwcsZFc+8LxFTAlIgNAq1O5/oZS5wFIqjApuQIV2lEseTeXR\nK/9ijJ87JyYDZ0UREdx6tV8nCIfD/PrJ70x/6xcMmzDi4mMYPLpflfzZf5s8N8Y+D+Bw2vlzxnKG\nnjXYYlTpNGqSxoQlTzH9zZ9Z8MOftOzYnK1rtzNv2mL83uhSjefeFRtNftd71zN14g98/cp3+Lx+\n9mzbgyffSyhoLlqcbgcBX9ByXyAUDKFCYSjHV1YpBZ4vgZIuzz7wfIpkfozyfgWquBnLBc7e4F9U\nysR54P8NhQ2VczuEc4EQynEwkvF/iE3vm9UEWvkXY8WuneV5go6L02ZjZLfuuOz6bU00SikeOusZ\n5k1bVKSsF/24lKFjBnHLa1dXeL71y7OY/sbPbFi+CTEkxu8ezALqlcWd6mb01Scy+uoTAXNf4f+u\nmMDPH/2GzWZgd9gZ9+QFDBgZ6zZss9k4+YrjOfmK4wHI3ZXHG/d+wK+f/I7NYWfEv49m2awVljmG\n2h3UBmdSeaPRFbGKf2+TD7F3gcavoXLvheBqwAHu0yH1StgxvNR5VWA55I9nX1oHILAYtfsiaPpt\nnQlAq0/o9A7FuPabKXyzaiXhcrwngpmgzRsKkWS3EwqHGdS2PS+MPFmnaa4DLJ25nDtPfCRmle5K\ndvK/3x6l86Hl24wFmDrpe8Zf/zqhQLBoNV2SJi0zeG/jyxVK31AeCvM85OzMpXm7ptjslZ977dIN\nXD/4bnweP+FQGDEEh8vBf7+5m0OP6lHuecK7zoPAfKI3GgScR2A0ebXoiFI+wF7k4hnefRn4Z2JW\n3yqJC5JGgXcyMXsGkow0fhVx9im3jA2dWi3gXl+4pv8gXOX88SrgtAN78selV/DaKafz/QX/5rXR\np2vFX0eYN32xpb97KBhi/vTF5Z4nLzuf8de9ht/jj1H8TrcDu8OG3Wmn/UFt+HPG8irLXZLkNDet\nOrWokuIHaH9gG3NzGDP7qGEYpDdNo3WXFmWMjEYaPQCSAux9WnCBpCGN/hPdT1xRvv2S8RTYDyM2\nZbMbkkZEKndZbBYrgZBFXIKmymjlX4wDMpvyzmljOKxFS4zIY2a8h81kh4Pju3aliTuZ/m3a0iZO\nXV9NYkhrnIrTFXsjtjvsZaZZADPVwtKZy/lt8tz4pRMVYAhBf5BFPy3jnpMf44vnp1ZR8prhs+e+\nZunM5YRDYVRYEQqG2LU5m0fOe65C84ijG9J0GqRcDq5jIfUKpNl0xN6x9HFGOkbT96HJl5B0Otg6\ngr0HpN2NpD8OzoGA22JkEBzaHbQm0GafUliTvZuHZvzEjPXroh5y3XY7R3fszPMnjtK2yDrK7q3Z\nXNj12pjo2KQUF+9vfIXUDOsbQGGehwfPfIo/f12Ow+Uwx4up4Eti2IyY1Asut5OPtk4iOc1KkdU+\nSikKcwu5qu8dbF69Nabd7rTz4aYJNMpMS4B0+1DhAtSukyOr/L1PAG5wj8TQAWIVQmf1LAWlFHM2\nZfHdmlWkOBycemAPy1KNm/Py+GNTVswmcOMkN8+NOEkr/jpMk5aNuffjm3nk3GeLjhk2g/s/vTWu\n4gd46pIXWTLjLwK+YEzGzOKIiGXOHbvTzqqFaytkR68pfnjvVybc8ha5u/IIWhSfATAMifIoShRi\npEDmZ6j8V8A3HSQZ3P9CksckWrR6S4NT/koprp/2NT+uWYMnGMBmGExaOJ8Hhh4TU4D9jUULLBO+\nZXs9rMrezQGZTWPaNHWH/icezsfbXmXpzL+x2Qx6DjkgvgkHc9X/+5T55Qq2crjsljeHYCBU6io6\nHA6zauFagoEQ3ft0LlWeqvD7V/N5dtzLUfEIVjRtm0lmHSnwIkYG0uh24PZEi9IgaHDK/+f1a/lx\n7RoKg+YPNxgOEwyHuffnHzm+S7eo9MtWFb0AbIZBjtc6GZwmMSilWLNkPTk78zigb2dS0s3VvdPl\noPex5bMZF+QUIkb5nuasFL9hM2jTtWXc5Gj/zF/Nfac+YZ5HBJvd4M73bohJDVEdvHX/h6UqfofL\n3Ky+/c1r9BNsA6XBKf+v/llBYSD2h2s3DGZtXM/Ibvty8RzfuSvLd+6ICfwKhRWHNK+Yl4Sm5ti+\nYQd3jnyU7et3YLPbCPiCjH3obMbcfErZg4uR2boxqRnJ7PZUzAxis5seP226teSRr+607OMt9HH7\ncQ+Rvye6DOgDZzzFGyuew1vo543/fMCfM/6iccsMzr3zdIaOqXxBk63rrCNkbXYbQ07rT6dD2jPi\n38fQtHUTfB4fMz7+nTV/rqdjz3YMPWtwmRHDmv2fBqf8nTYbQmw6FBFwGNHudOcfehgf/fUnW/Lz\n8QaDCOCy27nnqGG4tTa4v4IAABuGSURBVEtnneHuUf8la8XmKBv8m/d9RJdeHek9/NByz2MYBte/\nNI5Hz/0//N6AGdFq9WUpgcvt5H+zH6FDj/jpkGdPnmeZADAcCvHp/33F1Ik/4Mn3osKK3Vv38OTF\n49m6bjtn3zq63PIXp9Mh7Vnyy18xx91pSdz13vVF8Qg7N+/m2oF3kr+nEG++l6QUF6/d9R7P//5o\nnS/3qKkaDc7V88wePUmyiMBVSnFE++jAn1Snk8nnXMBNA4cwsG07RnU/kHdOG8O5B5dfoWhqlvV/\nbWTLmu0xm6++Qh+f/6/ibpeDT+nHMzMe5KizBtGtd2cGjOyNM6n0G707zV2q4gcz6jYUiN0gDviC\n/PGNGYVcPGrYV+jjnQc/xuexzs1fFpc8eh6u5OjIXVeyi4sfOicqEO3FG14ne+sevPmmGdNb4GPP\njlz+d/WkSp1Xs//Q4Fb+fVq14bLe/Xhl/h8YIhgiKAUvnTTacjWf4nRyae++XNq7TM8pTQLI250f\nNwBqz47cSs3ZvU8X7nn/RgC2rN3GuENvjtvX6XYy8rJjy5zz0KE9LING3KlJ5GcXWHoOiWGwefU2\nOh1c8YyXPQYdwGPT/sOkO95hzeL1NG3ThAvuO4v+Iw8ne9seMpqn/3979x0fVZk9fvxzpqdCgFCD\nFIkK0kWMICAgggUUBcWCYERRV0XUXXVZsaAuorvigrqiYlnZL/rTFRDX3qWsVBWlVwOhhp4y7fn9\nkQEJMymQcieZ8369eIW5c+feE17h5LlPOQ8iwsIPloQtXgsGgiz6eDnGGB0PqMFiLvkD3J3RjaFt\n2vLN5o3EO530bXEqSW7t46yOWnVuSSDCNEaXx0n3E6hPX5xnMl/AG2EMwO6w43Da6dinLdc8OLjU\n6wQDQYLHbfhjd9g5rcup2B02crLDK4L6vX7qNKx90rG37X4Gk797HCh88pg0ciqTRk5FBFKb1uOP\n02/HZo/88F/ccVVzxGTyB2iSnMy17TpYHYYqJ0+8m1v/PoJ/3vM63jwvxhS2xus1TmHg7f3Lde2C\nvAJ+mbeKYIQibg6XgykLnyxTqzwYDPLQwIn4j5tCKjYYeFt/atVL4pf5q4vMznF5nJw7qAu16pV/\n5bgxhj/1e4zNv/x2dCOXbeu28+BFT3D2RR1Z+MGSIovY7E473S/vqq3+Gk5/vatq79Jb+vHUp+Pp\nOfRc2vVszYhHr+bFpU8Xu2F6WZWU/FweZ5m7Y9Yu3Rg2ywfA7w3w8fQv6di7LWOn3UpyvSTc8S6c\nbic9h5zLH1878eqjkaxZsoGta7PDdvDyewOkNKhFk/RGxCV6cLgcxCV5aNSiPndOvalC7q2iV8y2\n/FXNcma30zmzW8VumenyuOhwfluWf7WiSJ+80+2g9zXnlfk6vnxvsesHjpSf6HttD86/uht7tuaQ\nmJJYoeUhdm7eFbEbx+/zs2vLHqb9+AxLPvuJLb9m0fSMJpx1YfsKr06qok/MJv/thw4yfdlSlm7f\nRquUOozq3IVWdepaHZaKMve+ehtjuo/j8P5cvHleXB4XjVo2IPOJa8p8DU9SHL6C8LUl7nh3kV8i\ndru9UqZXturUImJtIneci3Y922Cz2Ti7f8dKWWymoleNTP5HitUV99i+cd9eBs+cQZ7fjy8Y4Mft\n2XywZhWvDrqCjLSSp+yp2FK/aT3eXDeVBXMWk71hJy3bn8JZF3bAZitsSQeDQUSk2J+118bP5N2/\nfRA2o8aT6OHU9s3of2PvSv8eGrVswHlXZDBv1v+OjivYHXbia8Vz8ajSZyodz5g8COwCeyoi0VHA\nTp24clX1FJE6wNtAc2ATcJUxJmzagogEgJ9DL7cYY0pdenkyVT335uXxyDdf8vG6NQSNoccpzZnQ\n+4Kwcsuj587ii40bwjZtaV67Nl8Mz9SBLlWqbeu389xtL7P8qxXY7DZ6DjmXO6ZkkpTy+25ea5du\nYGyPhyg4braQzW5j7LTR9Bveq9x1+ssqEAjw/nP/Zc4Ln5B3KI+MgV0Y+dgw6jZKKfM1jAliDj4L\nuW8UjlabIMSPQJLGIqLDh9Giqqp6PgB8YYyZKCIPhF5HqsqUZ4yp1GfKoDFc/e5MNu/fhy80pe67\nLZsY/PYMvh45qsgmKwuzfou4W1fWgQMc9HpJ1mmfqgSH9h3mzow/c3DvIUzQEAwE+fbdBWz6ZQv/\nXPr00cbD1+/Mj9jd4/I4MUFTZYkfCruUhtwzkCH3DDzpa5jDr0JuaCP2I/99ct/E2GohiaMqJE5V\ndcr76/oy4I3Q398ALi/n9U7a91s2k33o4NHEDxAwhlyfjw/WrCpybnFz+m0iZd7JS8Wuz/71DQV5\nRVfk+r1+stfv4OfvKn43r0P7DrNt/faI6xkqms/rY9Eny5k/ZxGHD+QWffPwKxTZYxcKXx/W1cDV\nUXlb/g2MMdkAxphsEalfzHkeEVkM+IGJxphZkU4SkVuAWwBOOeXEVjVu2JuDL8IqyVy/jzV7dhc5\nNrJDZ55dOK9IuWaX3c4l6afr5usqIp/Xxxczvufrt+eRtXpbxIqZwaDht1Vbj9byP/+qbsye8lFY\nt08wECRjYOkrxvMO5fF05gss/GAxdocdp9vJH/6RSd9re1TMN3WcFfNW8dDAiUcXo/l9Ae56YRT9\nR4TGJcy+yB8M7+lV1UCpmU5EPgcaRnhr3Anc5xRjzDYRaQl8KSI/G2PWH3+SMWYaMA0K+/xP4Pq0\nqlMXp92GN1i0dRTvcNK6XtEZFJmdzmLD3hz+s+pX3HYHvmCAc5qkMaH3BSdySxUjAv4Af7rgMdYt\n2xi2IfyxRKDZMeWc0zu3ZMh9A/l/z3xAwB/AZrMhAne9cDMp9WuVet8nr/sHSz79EV+BH1+Bn/zD\nBTx7y0ukptWt8M1i8nMLGHfJk+QeKNqyn3L7K7TJOI2mpzcBRyvwrw3/sCO9QmNRVaPU5G+MKTYj\nisgOEWkUavU3AnYWc41toa8bRORroBMQlvzLo1vTU0hLrlX4BBBqudhFSHK7uCS96PxvmwhP9r2Q\nsRndWZuzh7TkZLbs38/gt2ewLmcPKXFxjD7rbEZ16qKDvzFi36797M7KoXGrhmFz7L9//wfWLdtU\nYuJ3uh2c0iYtbK3ByEeH0XvYeSyYsxiny0GPIRnUb1r6JkB7sveGEn/RMYOC3AJmPjXrpJP/zi27\neO72l1m5cA3J9ZLJfPwaeg45lx/+uzRi9VK/L8Anr33FqInXI0njMHtvBY7dy8KDJJ1IO1BFi/L2\nccwBRgATQ19nH3+CiKQAucaYAhGpB3QHJpXzvmFsIrw95Goe//ZrPly7moAx9G7egod79Sm2/HJq\nQgKpCQksyd7KLXNnHa3bn5OXx+SF8znk9TI2o3tFh6qiiDffy9M3vsC8WT/gdDsI+AIMuW8gIx65\n+ugv/oVzF5N/OHzzHpvdhojg8jjpe10Pbp40PGJjoVnrNJq1TjuhuPZu34fT7Yg4YLyjmFr9pdm6\nNpvMNncfXbB2MOcwE676O0PvG8QpZzQJqz0EhU89R1Yni7sb1HkTc+gfhU8AjnQk8S7EpesDqqPy\nJv+JwDsichOwBRgKICJdgFuNMaOA1sBLIhKkcIB5ojEmvNB4BUh2e5jUbwCT+g04oc89u2B+2IYt\neX4/ryxdwu1dztFxgBrs+bumM3/OInwFvqOJ9r2/z6Vhs/oMyOwDQHLdJOwOW/hc/QQ3f54xhnMu\nOavC42pyWqOw+0Fh3Z0O50du9Qf8ARZ/+iO7s/ZwxjnpnNqheZH3n7z+uYjVQ9/92xymr4z8nifR\nQ7fLuh59La6OSJ3pJ/jdqGhUrtk+xpg9xpi+xpj00Nec0PHFocSPMWa+MaadMaZD6OurFRF4RVqX\nsyficQF2Hg6vyaJqBm++l8/f+jasamf+4QLenvT7nISLR/WNuNeuw+mgc7/K2dshLsHD8PFD8CT8\nPjPNZrcRl+Bh2P3hk+q2b9rJ9S3/wJPXTObFe15nTPdxjL/8Kfy+3xs1G5ZvingvY2Dzyq0M/eMg\n3PFujjy8eBLcdOjVhi79tQBiTaRNWgoHi3fmhid5g6F+QoIFEamqkHcon+IWOR67F0CzNk25e9po\nJo+eht1hwxhDXKKHJz78M05X5e3odvWfLqdRywa8PWk2e7fvo2Oftgx/eGjEEhCPD3uWnG05RSqQ\nLv38J2ZN+ejo3H6HyxFW3O2Iuo1T6H7ZMDr1acfH07+kILeA86/uTvfBXY+uZlY1iyZ/YOy53Vj6\n/rYiXT9xDgc3deqiXT41WHLdJGql1mJ3VtEnP5HCWvjHuuC6nnS/vCu/zl+NO95N64z0Kil+1nPI\nufQcUvJevjnb97Lhx81hpacLcr18+PLnR5N//xv7MHvqR2Gfj0vycMbZrQDo0OtMOvQ6s4KiV9FM\nf6VTuLvXtEsv57Q6dRGgTlwcd2d0Y2xGN6tDU5VIRLhz6k2441xHuzpsdhueRA+jJl4Xdn5cgoez\n+nWgbfczoqrqpd/rp7hJaccOGN8+eSSnd21V5H2nx8nk7yZUZngqSpWrtk9lOpnaPkodz+f14Svw\nl1gi+deFa/i/J//D1nXbaX1OOteOu4ImrRqd8L327z7Ah9M+49cFa2jetimDbh9Qpmmd5WWMYUT6\nnWRv2FHkuNPt4Iq7L2HUX68vcnzjii189+4Cmp7ehF5Xd9NunRqmrLV9NPmrGik/t4Cpd77Kl//+\nnmAgQMMWDRj70mg6nF+2Lo21SzfwygMzWL14HXUapnDduCvpe13xK2uzN+7gjq4PkH+4AG++D4fL\ngdPl4JmvHuG0s06tqG+rWCv/t5b7+z1GwB/Am+/Dk+gmNa0eUxY8QUItHbeKJZr8VUz7y8C/suyL\nn/Hm/97t4Y538/wPfyWlYW22b9xJo5YNilThPGLDT5sZ020c+bkFRT474tGrGHpv5IK0j175DPNn\n/xDW796qUwteXFLhy1oi2rtjH5+8/jXZG3bQvmcbegzJwOWuvAFpFZ00+auYtWPzLjJbjymS+KGw\nP7/xqQ3YuWV34cwXr58LR/bmjimZRfrwHx48iQVzFnH8f424RA/v7poeMaEOSh5O3qHIC8HmHHgT\nd5xWilVVo6zJXzv7VI2zfeNOnBESdDAQZOva7XjzfeQeyMOb72PuS59yRZ2RvDD2NQ7uPQTAmsXr\nwxI/FPat79maE/Ge7vhiKsXabVVaulmpstLkr2qcU9qkhbX6jwh70jWQezCfuS9+yh1dH6Agr4CG\nLRtE/GwwEKR2/eSI7106uh+uOFeRY06Xgx5XZkRcIKaU1TT5q0q1K2sPM558j6l3TWfBB4sJBCq/\nJn1K/Vr0H3l+kdZ4aQX6fF4/Odv38/Xb8xn+0BDc8UUTuTvOxYUjexOXGHnW0LXjruDsAR1xeZzE\nJ8fhjndzWpdTGfPizeX/hpSqBNrnryrNok+W8+iVzxAMBPAV+PEkekjv1IKnPnuoUlfGQuHeuu8/\n9yHvTf6Qw/tz6dDrTLauy2bLyq0lfm5AZh/ufeU2vn5nHi/e/ToHcg5hd9i45OYLuHnS8FJb8Vlr\ns9n402Yat2oYVlunqmWt2cZrD81kxXcrqdOoNtc8eEWpC8ZU9acDvspSfp+fqxrefLQf/Qh3vJvR\nz9zAwFsvrPKYVsxbxQP9H8eb541Y1sHlcTJ8/FCGPTAYKOwiOphziLgkT6X/sqpo29Zv57az/lRY\nwiI0A0lsQsv2zbjn5VurZPqpsoYO+CpLrV26MeK2gwW5BXzx1rcWRFRYsmHKgifoOTQDuzN8ENbu\nsNP/xt5HX4sIyXWTql3iB3hrwrvkHy661aQJGtYv38TYnuP55p35FkanooEmf1UpHE57sUXTnB7r\nkmmLds34y8x7+Nf652nbo3XhYiy3k6anN+bpLx4mpUFty2KrSL/MWxWxRDOAN8/L5NumVcmewCp6\n6TQEVSlO7dichNoJYXPfPQluLrnZ+u0yU9Pq8uw3j3Eg5yC+Aj91G6VEPC8QCJB/uID4pLhqtatb\ng+b12bZ+R7HvB3wBstZso1mbpsWeo2o2bfmrSmGz2Zgw+34SUxKIS/LgjnPhinPRa+i59Loqegrm\nJddJipj4A4EArz44g8trj+DKeplc2+xWvn13gQURnpxrHhwcNmPpWH5/gMQIq5tV7NCWv6o0rTq1\nYGbWSyycu5QDuw/QvlebatPSfOm+N/nvy59TkFu40cvurBwmjZxKYkoinfu2szi60nXq0467XxrN\nP25/mbyDRZ++HE47rc9JL/ZpR8UGne2j1HHycwsYkppJwXE7fAG0Pe8Mnv22+pRA9vsD/PPeN/jv\ntM9weVz4fQGan5nG43MfpHZqLavDU5WgrLN9tOWv1HH27dyP2CL375fUjx6NHA47dzyXyQ0PD2Xd\nsk3UbZxywpvJq5pJk79Sx6nbOAWJUONeBFp1bF71AVWA5DpJ1aK7SlUdHfBV6jhOl5Pr/nIlnuOK\ntbniXIycMMyiqJSqWNryVyqCq+4bREr9Wvz7iffI2b6PVp1acPOk4aR3bml1aEpVCB3wjSDX56PA\n76e2x1Ot5nYrpZQO+J6EAwX53P/5J3y5cQMATZKSmXhBf7o20QEypVTNon3+x8ic/T5fbtyALxjE\nFwyyaf8+bpz9Hpv27bU6NKWUqlCa/ENW79nNyt078QWL1kPxBQK8sXyZRVEppVTl0OQfkrV/P44I\n0/v8xrB+X+St+5RSqrrS5B9yRmoq3gi7TLntdro0amJBRLEhGAwSDEauPqmUqjzlSv4iMlREfhGR\noIgUO7osIgNEZLWIrBORB8pzz8rSJCmZS9JPJ87x+xi4TYQEl4vr23ewMLKaae+OfTxy5dNc7LmW\nAa5hZLYewxuPvM2urD1Wh6ZUTCjXVE8RaQ0EgZeA+4wxYXMzRcQOrAH6AVnAIuAaY8yvJV3biqme\ngWCQ15Yv5c2flnHY66VXsxbc2+08miRF3rRbnZyAP0Bm6zHs2Lw7rKa80+Pkzqk3cVFmX4uiU6p6\nq5KpnsaYlaGblXRaV2CdMWZD6NyZwGVAicnfCnabjVGduzCqc6n/bqocfvhoGXt37o+4mYgv38fU\nO16l60WdteqkUpWoKvr8mwC/HfM6K3QsjIjcIiKLRWTxrl27qiA0ZYWsNdn48n3Fvi8izJ+9qAoj\nUir2lJr8ReRzEVkR4c9lZbxHpMeCiH1NxphpxpguxpguqampZby8qm6an5mG013yVo7H7j2rlKp4\npXb7GGPKu+deFnDsDh5pwLZyXlNVY2dd2IH6zVLJWr0tYtePMYZul2nXm1KVqSq6fRYB6SLSQkRc\nwDBgThXcV0Upm83G5O8m0O+Gnjic9sKDAnanHZfHyei/jaBek7rWBqlUDVfe2T6DgSlAKrAPWG6M\n6S8ijYFXjDEXh867GJgM2IHpxpgnSru27uQVOzb/+hvzZi3C7rDTc0gGjVo2sDokpaqtss720aqe\nSilVg5Q1+esKX6WUikGa/JVSKgZp8ldKqRikyV8ppWKQJn+llIpBuo2jUhUs92Aeb014ly9mfAdA\n3+t6MHz8EOIS4yyOTKnfafJXqgIFAgHu6TWeLSu34isorF80a8pHLPviZ55fNBFbhA2DlLKC/iQq\nVYEWf7ycbeu2H038AL4CH1vXZrP4kx8tjEypojT5K1WB1i7dSN7h/LDj+bkFrF26wYKIlIpMk79S\nFahB81TiEjxhxz3xbho2r29BREpFpslfRb19u/Yz86lZPHXDFGa/8DG5B/OsDqlYPa7MwBXnKrLB\nkdgEd5yL867oamFkShWltX1UVNv482bG9hyPr8CHN9+HJ95NfK14nl80kXqN61gdXkRZa7N56oYp\nrAt186R3bsn9/7qTJq0aWRyZigVa2E3VCH/o+gBrFq8vcszusNHr6u48+K+7LIqqbA7uPQRAUkqi\nxZGoWFIle/gqVZnycwtYv3xj2PGAP8jCD5ZYENGJ0aSvopn2+auoZbPbivSdH8vl1naLUuWhyV9F\nLZfbSdeLO/++29eR4x4nA27qY1FUStUMmvxVVLvn5VtJO70xcYkePAlu3PFuzux+BsPHD7U6NKWq\nNX12VlGtVr1kpv34N1Z8v4pt67fTsn0z0ju3tDospao9Tf4q6okI7Xq0pl2P1laHolSNod0+SikV\ngzT5K6VUDNLkr5RSMUiTv1JKxSBN/kopFYM0+SulVAyK2sJuIrIL2FwFt6oH7K6C+1QEjbXyVKd4\nNdbKUZ1iheLjbWaMSS3tw1Gb/KuKiCwuSwW8aKCxVp7qFK/GWjmqU6xQ/ni120cppWKQJn+llIpB\nmvxhmtUBnACNtfJUp3g11spRnWKFcsYb833+SikVi7Tlr5RSMUiTv1JKxSBN/oCITBCRn0RkuYh8\nKiKNrY6pOCLytIisCsX7vojUtjqm4ojIUBH5RUSCIhKVU+hEZICIrBaRdSLygNXxlEREpovIThFZ\nYXUspRGRpiLylYisDP0MjLE6puKIiEdEfhCRH0OxPmp1TKUREbuILBORuSd7DU3+hZ42xrQ3xnQE\n5gLjrQ6oBJ8BbY0x7YE1wIMWx1OSFcAVwLdWBxKJiNiB54GLgDbANSLSxtqoSvQ6MMDqIMrID9xr\njGkNZAB/iOJ/2wKgjzGmA9ARGCAiGRbHVJoxwMryXECTP2CMOXDMywQgakfBjTGfGmP8oZcLgTQr\n4ymJMWalMWa11XGUoCuwzhizwRjjBWYCl1kcU7GMMd8COVbHURbGmGxjzNLQ3w9SmKiaWBtVZKbQ\nodBLZ+hP1OYAEUkDLgFeKc91NPmHiMgTIvIbcB3R3fI/VibwkdVBVGNNgN+OeZ1FlCao6kxEmgOd\ngP9ZG0nxQt0oy4GdwGfGmKiNFZgM/AkIluciMZP8ReRzEVkR4c9lAMaYccaYpsAM4I5ojjV0zjgK\nH61nWBdp2WKNYhLhWNS2+KojEUkE3gPuPu4JO6oYYwKhbt80oKuItLU6pkhE5FJgpzFmSXmvFTN7\n+BpjLijjqf8GPgQersRwSlRarCIyArgU6GssXqhxAv+u0SgLaHrM6zRgm0Wx1Dgi4qQw8c8wxvzH\n6njKwhizT0S+pnBsJRoH1rsDg0TkYsADJIvIW8aY60/0QjHT8i+JiKQf83IQsMqqWEojIgOA+4FB\nxphcq+Op5hYB6SLSQkRcwDBgjsUx1QgiIsCrwEpjzN+tjqckIpJ6ZNaciMQBFxClOcAY86AxJs0Y\n05zCn9cvTybxgyb/IyaGuip+Ai6kcCQ9Wk0FkoDPQlNT/2l1QMURkcEikgWcC3woIp9YHdOxQgPn\ndwCfUDgg+Y4x5hdroyqeiPwfsAA4XUSyROQmq2MqQXdgONAn9HO6PNRajUaNgK9C//8XUdjnf9JT\nKKsLLe+glFIxSFv+SikVgzT5K6VUDNLkr5RSMUiTv1JKxSBN/kopFYM0+SulVAzS5K+UUjHo/wNa\nv0s7YKkLBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa1e876fb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2d = PCA(n_components=2).fit_transform(X)\n",
    "data2d_df = pd.DataFrame(data2d)\n",
    "data2d_df[2] = ypred_kmeans\n",
    "plt.scatter(data2d_df[0], data2d_df[1], c=data2d_df[2])\n",
    "plt.title(\"Plot da clusterização K-mean, K=3\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BVN6TUPg2YIK4USm3YSSeW6XTLQ8RGzSdBp7ZKM9XYCQj7nMRZ/0y5WrlXw3m\nbN7ER6tXRmzvmJpGUcDP9ry8ctuwGwYD27XHabNx9mGHM6p7j/ox6aZpUCQ3TSYQiN7ZaJJey6EO\njFYQ3Bq5XfnBaIbyr0RlXU6xGUflQP4UlJmFNPlH7Hbzn7MU//7JXwUENqJy7kea/q/C4lmK/62w\n43sh92FM3+8gbsTZHxJG1airqIgd3KMR9+gaa7Om0bF9KkiOx8PyXTvJKjpgx5u6eEHUEA8Zubnc\nf/wJJDucxWsBYoWBdtpsvDz2LF4eexaje/SsULhojaY06W2actjAHtgdJXvSCUkuzr759Fo9tiRP\nIjLMghNcwxCjGSp/MpHeO0VQ+D4qNAGqlEIFtqACGWFVPomynx+836KU9dyp4A7MnPsxd5+Mufdv\nKO+cErWV8oV6/FGif3pmQNF7qNz7UXvOKJblYEH3/MvBVIp//fQ97638A7th4AkEaJ6YyCndurM5\nJ/qSe4fNoHVyCnMuvZwZa/5kV0E+iQ4Hz/3+W4lAcG67ncuO6oczSmRQzcHN/JmLef2B99ixMZP2\nPdtw8QPn0u+UvmXu84/3b+G+Mf/HppUZ2B02/F4/Z908huPPGVSrsoprOCrldsh/ytqg/OA6Hkl9\n3PoeiO7ZhtghuB0V9KD23QTBPYBC2TqEevbRY2dZnjQmKrgdtWdsaL4hAMHNqOw/UCl3YCRdGKpa\nhufNflShNUldMA1JuamSZ99wkfoafKxfv35q4cKF8RaDFxf+zrO/xw7iFo0kh4OFV1yDq9QK4Nnr\n1/LIzz+RkZtDisvFlUf358p+A3RvX1OCH977lacuez4iBEPPft14+PO7aNqqbHv35tUZ7N2eTfej\nOlfbhbMyKOWFwBawpSNhLplm9rVWmIaIF4ALmn8Fe0+3FkIVIyBp4OgPvu8oOZ8g4DgaI/0dzJz7\noehDIl4SkoS0nI+I07L3Zw62kr2Uh60DRov4ZQKrKURkkVKqX3n1dM+/HF5ZuqhSit9tt3PPccMj\nFD/AyG49GNmtB/5gEHtoUlijCUcpxZTbXo8ae+evReu5fcSDTF3+dJn3TqfD2tPpsPa1KWZURFzg\niFwhLMnXory/UDLmvRsSJyDeH6J43yjAB87+4Ps5bD8DSERSH7a++uYRc3QQ2AiOQxCxoVJug9yH\niTT9lMZVTnnjQtv8yyHXW7HgbA7DYESXbrwy9izOP/zIsuvabFrxa6LiKfSSvStGL1VB5uY9rJr3\nFwAbV2zh3cc+5eNnvmTPtr11KGXlEEcvpNnLYO8NGCBNIflqJOUuVHAnUZWy8kHR+5Q02SgQ40AI\nZVuMAHTKD8aBhXBG4rlI2pO8qq1SAAAgAElEQVRgPzQUATSJyKg0CZB4flVPsUGie/5lYCpF59Q0\n/soq/8Fy2GxMOf2MOpBK05hxuZ243C4K80pnhgohwq7Nu/n109+ZMflrAv4gNrvBy3e/xa2vXMOJ\n5x1b5WMr5QX/chAX2A9HpOb6huLshzSPst7AeTSqKDFKQhQDgpspGSZBgfKiCt8GezcIRPEwwgmu\noRH+9JIwEkkYabUS2ILKujAUdyc06nAdj2jlrwFrQdYln37IltyKxVHv2az+LN7QNFwMw+CcW0/n\n7Uc/JuCLNGkEA0HEEGY8Pwtvka94G8BTl71A/1P6ktK08q6dZtHXkHs3Vo/YtHrITacgjkOqczrl\n4xoOtm6hSeH9o+wEsHex3EcjAqF5oegrVDCDyNSJdkvxpz5V5iHF3hFa/AC+XyC4Cxx9EMehNXI6\n1UWZ+RBYD7ZWSKyRTQ2hlX8M3l/5B8t37cRTAXu/AI+ceHLtC6U5KLjwvrPxFHh5/8nPSsyRuhKd\nDD69Hyt/WY2vKHJOwGYzWPDVEk68oGRMnY0rtvDtG3PwFnk59syB9Bneu4TZUQU2Qs4dlDC/qELU\n3otQTf+DBHeB4zDEUfOhFURskP4mqmA6FH0G2CBxvDXZmxWtJ+6MMiII4TgKo+lLFTyu3Xrx1BOU\nUtZK6fwplheU8qGcg5G0/yBG7azT0Mo/Bh+vXhl1ojfBbqdzahrrsvYSVKp44vamWTO5fcixjOja\nPQ7SahoThmFwxWN/44zrT+XVe99m4TfLcSe5OP3qUzjzhtH8+2/PxAyTUHrzp8/NZNqdb+H3BVCm\nyaxXf2DomQO587Xril8AqugDok+c5kD2VSgMQKGcA5Cmz9d43HwRN5J8NSRfXWK76TgC/MsooejF\nDirWJO/aGpWrTvF8CflTAc+BF75vHirnbqTps7VySD3hGwObEf3SGCI8dcponj9tLC67Hb9p4gsG\nWZu1lxu+/pKZa9fUsaSaxkqL9unc8dr1vL99Kq+tfY4zrj+VB895kl8/i+4CHQyYDBh9IIRA9q59\nTL3jTbxFPsygiVLgKfDy6yfzWfJ9WGhkcy/Rlb/CMsUUAR7w/Y7Kr1jPuiyU8qI8s1CFH6OC22PW\nk6Yvgfs0wAkYYD8Cmr6GFT8yCrZoOaQaBqpgGpFmLB94f0CZubVyTK38Y3De4UfitkemXUtLSODQ\n9Ob83y9zIkxCnkCAx379ua5E1BxkfPbcVyz+9g/8Xn+J7SKCM8HBrS9fVcLev3DWsqgRQD0FXn7+\ncN6B/Z3DwoKQlYUn5IFTdZRvGSpziJXhKu9B1O5TMPOejlpXjGSM1MeQVsuQVssxmn+E4ewDiRcQ\nNWdu8vXVki2umDGcSsQGZs3lbw5HK/8YnHloL07s0hW33Y7TZiPJ4aCJy8WLp41DRNgcI4H71tyc\n4iH57PVrGfHGqxw6+b+MeP0VZq1vwMNSTa0yf+Ziru1/J2emT+SWYfez4tfIwLdfTv02IrsXgGET\nnvn1YU48v6St3+GyQxSXYsMQnO4w003CyWA/hMgQDVFQZbs+q8BWlGe2lc2qdJkKoLIngcoLZbcq\nArxQ8BrKGzvJiYithKlJUm6DxItB3IATpBk0+SeScGL58tdXnIOJro4TwFZ+OtiqoG3+MTBEePbU\nMazM3MX8bRmkJyYysmt33A5rNNAqKZkd+ZGB21okJiEifL32L2755qvi0cGGfdncPGsmT4wYxWk9\na9mDQtOgKL2i94+fV3PXyId49Kt7OfL4XsX1/N7otm6bw05KlJW8A0YfjTIjQxs4XA5OvmhY8XcR\nBzR7A1X4MXg+t0YBgXVgbiu1px0SRkSVQakgKudO8MwCcQBBlK0b0uyVAxE4fQuJnuGqCFX0PiCo\nvH9btnsjHZKuRhLPj1gTI2JDmtyGSrnRcteU1Bp1S40Hknw9yvv9gVAVCOCCJg/EjHxaXRr2FasD\nerdsxd+POoZxhxxWrPg3ZGfhihKPx223c8NAK1nDv+dGNws9PlebhTQHiLWi11vkY8odb5TYNnzC\nEByuSFNkepumUbN7Jaa4uf/D20hIdOFOSSAhyYXD5eDiByfQ/aguJeqKODGSzsNIfwuj2VSk6WQr\nnn7xqlc3GM2R5Fuin0fBa+CZzYG8tUUQWIPKuSv8rIie8hsI7kBlX2klbCcA5i4rH0DBlOj1sV5a\nYjRt8IofQOwdkOZfgPt8sPcE1wlIs+kYtRgVVPf8K0me18s5H7xDjqfkqkRDhNsGH8sFodW9W2ME\nfcsImYX0Ct+Dm9ysPL55/Sc2/rGVvTuyo9bZtGJLie/n3XUmcz9bQObWvXjyPTgTHNjsNu5684aY\n91P/U/ry3o6p/PbFInxFPvqN6kvztuWnQhRHL2j+jeUJFNgIjqMQ91jESIq+Q9GbRK7U9YP3Z5RZ\nYO3n6BfDU8cdCsBWev8iKHgRlXRpjXsY1UfE1hpJLSPEdQ3T6JT/N+vX8dRvv7AtN5euTZtxx9Dj\nGFqDmbE+/+tPvIFARIiqBJuNjmlp+IJBZvz1Jy6bHU8w8kZvmZSsFf9BzsYVW7j5uH8Q8AWKF2pF\no7SSTmqSyAuLn+CXj35j+ZzVtO7aklMuGV5uoLfEFDcnnl/5lb9ia265YFYEs/QK3XC8QBJiJKES\nr4DCyRwI2+AG59Hgj5HWWwXBzIodykFTZRqV8p+xZjV3fze72D//j8xdXPH5p7w0ZhzHdexcI8fY\nkJ0d1f8/YCrWZ2Xx1Lxf2bxvX1TF77bbuWngkBqRQ9NweeLSyRTklKUsrXy9f7t/fMR2p8vBiRcc\nF7GQK+64hoHnUyIid9raWrF8ALNwBhRODatjgDgh9XHYdz34syLbFWkYSdsbIA3fWBZCKcW/f50T\noZg9gQD//mVOjL0qzm8ZWxn37pu8vnxJVKul3WaQkZvLpn3ZFAX8EeXp7kTuO/4Ezu7Vm2U7d7Bq\nd2aF8plqGhcFuYVsWLY5ZrkzwUFyWhKX//sCRvzt+DqUrOqYBW+A5zNKKn6blSUr9VFEBKX8kPcg\nlmlnfz3TmuAsfBVJvolI9003JB4cJp940Gh6/r5gkMyCgqhlG7Kj9CgqwYLtGfx9xscxQz04bTa6\nN0tn5e5dUeskORw8P/p0PIEAA6a9QCBoYqJIdSUw9fQz6NWiZbXk0zQcDJsRc86zaetUXlryJE3S\nU6L659dHTN8CyHsoSomCZh8jjm7W18B6oidV8YPnByTlDkh7BpX3iJX3V1Ih6Qok6fJalP7gptH0\n/J02GynO6PG4WyVXPDaGNxBga04ORf4Dvfcn5v4SU/GnuRK48Ig+vHXmeFJc0Y+vlDUCuerLz9jn\n8ZDv91Ho97MjP48LP34fT5SRgqZx4k5K4KgTDsdmL/noORMcjLr0RJq2SquU4q/r0aNSPmuhVmCd\ndezc/4tR0wT/4gNfjdTYYRlCIZol4QSMFt8irVZjtFqAkTypUXjy1FcazZUVEa7pPwB3qSQqFbWz\nK6WYvOA3jpn6PKe+9RrHTH2eh+f8SNA0Wbs3+uo7l83G7Isu5R/Hn0CS08lFR/aNWBUsQMukJJbv\n2kEwyoMaME2+37ih4ieqafDc9uq1tOrUAndKAi63k4QkF4cO7MGF951d4TbmfDiPi7pdy0j7uUxo\nN4kvpnxT6y8Cs+hzVOZAVPalqD1no/aMgWBG7B3CYu2IrQ04DifS2OBGki4tsaW2/No1JWk0Zh+A\ny4/qh2kqnl/4O56An2Sni1sHD+WMQ3uVu++7K//g+QXzS8wZvLNiGclOJx1SU8nJjEw4YTMMUsN6\n+yd27srEvkfx8pJFOA0bCkhxOZk29kzeWL4UX7B0xiJL+WcVxYjdrmmUpLdpyit/PsOS71awc2Mm\n3fp25tAB3SvsBTZ3xgIen/hc8dqArB3ZvHjLa5iBIGOvGVUrMiv/asi5lxLumMH1WHF3YuA6qcRX\nSXvOWuEbWGstBFM+SJ6EJOiIuPGgUebwDZomBX4/yU5nhfPjHvfqVLblRQZQSnY4eWbUaVz71ecl\nTD9uu50rju7PTYMiRxWZBfks3L6dZm43A9q1xxDhuw3ruXHWlxT6S5p4Eux2Pp1wIT3TIxfpaDTR\nuPzwm9m8KrLHndo8hQ92vVwrrsRmzr1Q9BGRdvsELFfOUnpEmiItvosajlgF1kFwNzh6I0aTGpf1\nYKeiOXwbjdknHJth0MTlqlRi9D2F0SeLCwN+hnToyOMjRtE6ORlDhBSni2v6D+SGgYOjDrVbJiUz\nukdPBrXvUCzD8M5d6N2iZQmzVKLdwWnde2rFr6kUOzbsiro9L7ugzHUD1SKYSdQJW7ERNSaQ2ofK\nHIryLYjcxd4dcQ3Wij/ONCqzT3Xo1aIlS3buiNjeNiUFp83GmJ6HcFqPnviCQZw2G1/8tYZh06ex\nLS+XVknJ3DJoCON7HxGzfZth8MaZ4/lg5R98/OcqnDYb5x9+JGN61o8MQpraZ+6MBbz3+Gdk7cjm\n6BFHcuF9Z9OyQ+Vf/G27tWbTysgUhinNknG5a8kt0jUcfPOJWIWrvERXIwooQmVfBS3naXfNekij\nNPtUhUU7tnHxJx+WsPkn2O38b9RpEQlaZq5dw23ffB1hBnpg2ImcW8YLQHPw8uHTn/Pa/e/hCUXl\ntNltJDZxM2XZkzRvV7kUoL99sYiHJzxdopfvSnRx1dOXMGZS7djPlSpC7TkDgts5kG7RDfbOoXg8\nMZAkJO1/iKueLUprxNSp2UdERonIGhFZJyJ3RSl3ich7ofL5ItK5Jo5bkxzTph3vnD2BYZ060zIp\niYHt2vPK2LOiZuaK5vpZFAjw1Lxf60pcTQPCU+hlepjiByvvbmFeEe8+9mml2xs05hjuefsm2vVo\ngxhCi/bpXP/cZbWm+CGUbSv9I0i+DuyHg3MIkvYfcJ9N2aGgJbaLZxVRgY2Y+S9g5k1G+XWY9KpS\nbbOPWH5Zk4GTgQxggYjMUEqFB/S+DMhWSnUXkfOAx4AJ1T12TXNkq9a8Oq58d7toE8MAuwsLCJpm\n1CxgplLkeDwkOZ04o0QE1TRetv65LcKvHyDoD7I0PKNWJRgyrj9DxvWvrmiVQoxkJPlKSL7ywEYz\nD5U/GVT4yt0wVACcA2pMBrPgVch7GggCClXwEip5EkbydTV2jIOFmuj5DwDWKaU2KKV8wLvAuFJ1\nxgGvhT5/CJwkDTi6Wfsm0SeqWiYlRVX8n6/5k0Evv8igl1+i70vP8cicHwlEibOuaZw0bZWK3xe9\n99siSihmT6GXV+59m/PaT+Lctlfwwi3TKciJ7pAQb8RIQdI/AMfgUiV2IAFSH40dCTQGSpko31KU\n91eUeeC8VWBrSPF7sWLeBwEP5E/RI4AqUBMTvu2A8NmnDGBgrDpKqYCI5ADpwJ7wSiIyCZgE0LFj\nxxoQrXa4Y/BxJRK1gGXzv3XQ0Ii6P2/exJ3fzSqu6zfhrRXL8JtB/jn8pIj6msZH83bp9B3Wm6U/\nriiRkMWV6OS8O84oUVcpxR0jHmTd0k34PZZb8OcvzGLR7GW8uOQJ7I7656Mh9o5I+nRMMwi+38D3\nI0gTxD0OsVfuOVb+tajsy6xsXyGTkWpyP0biOeD9nqijC/woz2zE0aMGzubgoSZ6/tF68KV/oYrU\nQSk1RSnVTynVr0WLFjUgWu0wqkdPnjx5FB2bpGKI0DYlhYdOGBHV2+eZ+fOiJnV5b+WKCJ9/TcNi\nz/YsvnjpG2ZO+47szLLzrN773s0cc3IfHC4H7uQEklITue7Zy+gzvHeJekt/WMHGFVuLFT9YGbwy\nt+xh3ueLauU8agrDsGEkDMVoci9GyvWVV/wqiMqeCObOUJrHfMADuf8KpYUUoqsS0auCq0BNdCMy\ngA5h39sD22PUyRARO5AKVC/aWi2zNSeHtVl76ZyWRtemkSFlR/c4hNE9yk/HmJEbXSkYImQXFZHo\niMzMpKn/fDb5K6bc/gZiCCLC5Bte5uapVzHiwuiROJOaJPLQjLvYtzuHnD15tOveOmovft3ijREJ\n2gGK8j38tWg9x51VclBtmia7Nu8mMcVNavMG7jfv+z2UxjCiAFX4LpJ8DeQ9EaXcBgmn1LZ0jY6a\nUP4LgB4i0gXYBpwHXFCqzgzgEmAecA7wvaqnPqb+YJCbZ83ku43rcdps+E2Tfm3a8eKYcVVS1Ie3\nbMUPmzZEDHNshtAiqXK2UE39IGPtDqbc/iY+T0kl/Z8rXuTok46gWeumMfdNa5FKWovUmOWtOrfA\nmeCgyF8yFEhCkos2XVqV2Lbg6yU8ddkL5OcUYAZNjjiuF3e/dUOZ7ddrVC7Re/YmmHsRW2tUk39A\n7kOheqGnKuVWxN4lyn6asqi22UcpFQCuA2YBq4H3lVIrReRfIjI2VO1lIF1E1gG3ABHuoPWFZ3+f\nx/cbN+ANBsnz+fAEAizYnsFDP/0AwLsrljPyjVc5/Z03mLd1SzmtwS2Dh5IQI9ic9vppmPz0/lyC\ngcg4TWIIv37yOzs27OLJy55n4iHXc+cpD7H0h4p79Awe2w93shvDOKAERayk68MnHAglsnnVVh48\n5yn27sjGW+jD7w2w/KeV3HPqo9U7uXji6AcqiilU3MXxf4zEc5EW3yApt1t/Lb7GSJpYt3I2EvQi\nr1IcM+V5sj2RgdacNhtNExLYVSpnwImdujBt3FlltvlH5i4e+2UOKzJ30TIpiesGDGLsIYfVqNya\nuuP1f77HWw9/hGmWfHacCQ7OuXUsnz47E0+BFzNoeXS5Ep3c9NKVMU1Cpdm5KZPHLnqWP39fCyJ0\nPbITd75+PYkpCfi9AVp3ackz10zlq2nfFR9jPwlJLv77y8N069OZP39fy/tPzGDHhl30Gd6bc249\nvUL5e+OJmf885L8E7H8G3WDvjqS/o1cJV5CKLvLSyj+MPK+X/tNeiBp9syy+PP8iDgslZPEE/ARN\nRZJT36iNlXVLN3LT0Psi4ug4ExwcNeIIFsxcEvFiaJKezPs7p2GrxGgvf18BSiny9xXw0LlPs2nl\nVgxDSG3ehLSWqfy1cH3EPkmpbu5+80a8RT4en/gcviIfSoHdacOd7ObFxY/TsmP9daYAUN55qMK3\nLY8f16lI4hmIRM+VoYmkosq//vmNxQGlFI/P/ZnpSxcTjOF/bxA9DxHAo7/8xNMjR3PHt7P4detm\nUNC7ZUseHzGKHumVW7qvqf9079uFcdeN4rPJX+Pz+BERHE47Fz0wnk/+NzNC8QN4i3zsyciiVaeK\nK97ktCSCgSCT+tzK3u3ZqFC7mVv2sHdHNs4ER8S8g88ToGufzlx9zB3FIZ8BAr4gBTmFvP7gB9z8\n0pV8++YcvnnjJxxOB6dediLHnT2oVqKBVgVxDUZcpdcNaGoarfyBN5Yv5fVlS/BG6fE7DAOHzYaB\nkO+PHjHRVIpzP3yXjNyc4oQty3ftZPyH7/DTJZeTmlA6N6mmoXPFYxcx7NwhzPnwN2x2g+EThtLl\n8I78+N5c9m7PjqhvBhXJTcue4N+1eTdrFqwjvW0zeg3uiYiw6JvlFOQUFSv+/YgINocNW9AkGJoc\ndiY4OfasAfh9fjwFkfknzKDJwllLuW/M/7Hi1z/xFFjhJlb8spqFs5Zxy9Srqno5NA0QrfyBKYsX\nRCR+B8uf4MIj+jCx79Hc891s5mZERlIEGNGlG0//9muJTF0KK6/wJ3+uYmLfo2tJck086XlMN3oe\n063EtvPuOpMnLp2MNyyOjzPBwbFnDSSpSWLUdkzT5Jmrp/LN6z/hcNpRSpHerhlPfHs/e7ZlRdj1\nAQK+AMefM4jktCR++eR38rLyMYNB5n66gGU/riTgj266dCY4Syh+AE+Bl+/e/pmzbjqNzr07RN1P\n0/jQyh/ILorsJYHli3/bkOO49/tvWLgterq6I1u2wmGzRU3R6AkEqp08XtOwGDZ+MDs3ZfLmvz7A\nMAz8vgADxxzDzVNi96pnT/+R79/+Gb/XX+zjv33dTu44+SHsDluJF8l+EpJdDDj1aIadO5hfPp5P\nwBdAKUXAHywRQK7EPokuOhzSNno+AKVY+v2Kait/FViHyn0UfAvBSIHEi5CkK/QirHqIVv5A39at\nmRelV9+uSSo78nKZtW4tvijK3Q6s3J3Jmp+jx+pJdDg4slXr2hBZU4+ZcPs4zrhuFNvX7aRp67Ry\n/e4/ffarEj1xsEw0W//cFrW+M8FByw7NOe6cQSz4eilFBd7y8/cKnHXzaSilWPzt8oiRgc1uo0l6\nZNatyqCC21B7x4cWaikwPZD/PCq4GUmNlehdEy+08gfuPW444z94F18wQFApBMu188FhJ7J4546Y\nE2EBAKUIRpkrsBsGTRPcjOlZ/ipgTcPC5/Ex69Uf+OmDeSSlJjLmqpH0P6VviTout4suR3SqUHuF\neRXP4ZyQlMDZN5/G+NvG4nQ5yNqRjVkB77SEJBdrF29gyfcropqEDMNgcCWihCr/nxDYAPYexTF1\nVMErVl7eEksaPVD0OSr5ZjB3oQpeBv9WcB4G7gkYTp3/Il5o5Y+VxWvGeRcyecF85m/bSp7XR77f\nx3VffU6n1DQClXD9NERIdjg4pXtPbh9yHAl2Hb6hMeH3+bn5+PvZvGprsTfNom+WM/7W07nkwapF\nKT/2rIF8+uzMEkHfYpHSLImJ/zqv+HuvIYdEj3VWWm5PgCXfriDgL3kMu8NGSrNk/vXZnbiTyndM\nUGYBKvtK8C+3UjiqIMrZD2n6vLWNaIu0XKiC96BwKsWZwIr+gKL3MW3dkaYvVjoOkKb6NMocvlWh\nW7N0LjvqGPZ5PMVePQV+P6v27MavKh5+2SbCjxMv57ERp9A8MfoEn6bh8tP789iyOqOEG6W30Gul\nZ9wZ6eVTEc676wzS2zTDlWj5stscse3jpdM+djm8IwPHHENCYtl+8MFgMELxg+U19NaWFzl0QMUi\nYqq8/wP/UsBjBV/DA74FqLynwN4TiCK78kLR60SkgAQIrkNl/Q2lrA6WUiYqsAUV3F0heTRVR/f8\nw5i8YH5EBM7KIiK4dW+/XmCaJj9/+BuzX/8JwyaMuvREhozrXy1/9rkzFkTY5wEcTjt/zFnNsHOH\nRNmrbJo0S2HK8ieZ/dqPLP7uD1p3bsnOjZksnLUMn6dkqsbz74lcTX7P2zcyc+p3fPnSN3g9Pvbt\n2kdRvodgwOq0ON0O/N5A1HmBYCCICppQgVtWKQVFnwGlXZ69UPQRkv4ByvMFqHAzlgucR4NvaRkN\n54FvLgobKudOMHOBIMpxOJL2X8Sm581qA638w1izd09FRtAxcdpsjO7RE5ddX9Z4o5TioXOfZuGs\npcXKeun3Kxg2fjC3vXJtpdvbvDqD2dN/ZMvqbYghEX73YCVQryruZDfjrj2VcdeeCljzCv+9ago/\nvj8Xm83A7rAz6YmLGDg60m3YZrNx+lUjOf2qkQDk7s1j+v3v8vOHv2Fz2Bn19xNY+euaqDGGOhzW\nDmdCRVejKyIV//4iL2LvBk1fQeXeD4H1gAPcZ0Hy1bB7RJntKv9qyJ/MgbAOgH8ZKusSaP51vVmA\n1pjQ4R3CuP6rz/lq3VrMClwTwQrQ5gkGSbDbCZomg9t35LnRp+swzfWAFb+s5u5TH4nopbsSnfxv\n7qN0PbJik7EAM6d9y+QbXyXoDxT3pkvTrHUab299sVLhGypCYV4ROXtyadmhOTZ71dveuGILNw65\nF2+RDzNoIobgcDn4v6/u5cjje1W4HXPvBeBfRMmJBgHnsRjNXi7eopQXsBe7eJpZV4DvF6zsW6Vx\nQcIY8MwgYs5AEpGmLyPOYyos48FOnSZwbyxcN2Awrgo+vAo489De/H75Vbwy9iy+vejvvDLuLK34\n6wkLZy+L6u8eDARZNHtZhdvJy85n8g2v4CvyRSh+p9uB3WHD7rTT8bB2/DFndbXlLk1iips2XVpV\nS/EDdDy0nTU5jBV91DAMUpun0LZbq3L2LIk0eRAkCdg/WnCBpCBN/lGynrhK+PZL2pNg70tkyGY3\nJIwKZe6KMlmsBIJR1iVoqo1W/mEckt6cN88cT99WrTFCw8xYg81Eh4OR3bvTzJ3IgHbtaRcjr68m\nPqQ0TcbpinwR2x32csMsgBVqYcUvq5k7Y0Hs1IkKMISAL8DSH1Zy3+n/5tNnZ1ZT8trh42e+ZMUv\nqzGDJspUBANB9m7P5pELnqlUO+LogTSfBUlXguskSL4KaTEbsXcuez8jFaP5O9DsM0g4C2ydwd4L\nUu5FUh8D5yDAHWXPADi0O2htoM0+ZbAhO4uH5vzAnM2bSgxy3XY7J3TuyrOnjtG2yHpK1s5sLu5+\nfcTq2IQkF+9sfYnktOgvgMK8Iv51zpP88fNqHC6Htb9YCr40hs2ICL3gcjt5f+c0ElOiKbK6RylF\nYW4h1/S7i+3rd0aU25123ts2hSbpKXGQ7gDKLEDtPT3Uy98/AnCDezSGXiBWKXRUzzJQSjF/Wwbf\nbFhHksPBGYf2ipqqcXteHr9vy4iYBG6a4OaZUadpxV+Pada6Kfd/cCuPnP+f4m2GzeCfH90eU/ED\nPHnZ8yyfswq/NxARMTMcEYkac8futLNuycZK2dFri+/e/pkpt71O7t48AlGSzwAYhpTwKIoXYiRB\n+seo/JfAOxskEdx/QxLHx1u0RstBp/yVUtw460u+37CBooAfm2EwbckiHhx2YkQC9ulLF0cN+Jbt\nKWJddhaHpDePKNPUHwacehQf7HqZFb/8ic1m0HvoIbFNOFi9/t8+X1ShxVYOlz3qyyHgD5bZizZN\nk3VLNhLwB+l5TNcy5akOv32xiP9MerHEeoRoNG+fTno9SfAiRhrS5E7gzniLclBw0Cn/Hzdv5PuN\nGygMWA9uwDQJmCb3//g9I7v1KBF+OVpGLwCbYZDjiR4MThMflFJsWL6ZnD15HNKvK0mpVu/e6XJw\n9EkVsxkX5BQiRsVGc9EUv2EzaNe9dczgaH8tWs8DZzxuHUcEm93g7rdviggNURO8/s/3ylT8Dpc1\nWX3na9fpEexBykGn/B0DuiUAAByKSURBVL/4aw2F/sgH124Y/Lp1M6N7HIjFM7Jrd1bv2R2x8Cto\nKo5oWTkvCU3tkbllN3ePfpTMzbux2W34vQEmPjSB8beOLX/nMNLbNiU5LZGsosqZQWx2y+OnXY/W\nPPLF3VHreAq93HnyQ+TvK5kG9MGzn2T6mmfwFPqY/o93+WPOKpq2TuP8u89i2PiqJzTZuSn6Clmb\n3cbQMwfQ5YiOjPr7iTRv2wxvkZc5H/zGhj8207l3B4adO6TcFcOahs9Bp/ydNhtCZDgUEXAYJd3p\nLjyyL++v+oMd+fl4AgEEcNnt3Hf8cNzapbPecO+Y/yNjzfYSNvjXHnifbn06c/SIIyvcjmEY3PjC\nJB49/7/4PH5rRWu0m6UULreT/817hE69YodDnjdjYdQAgGYwyEf//YKZU7+jKN+DMhVZO/fxxKWT\n2bkpkwm3j6uw/OF0OaIjy39aFbHdnZLAPW/fWLweYc/2LK4fdDf5+wrx5HtISHLxyj1v8+xvj9b7\ndI+a6nHQuXqe06s3CVFW4CqlOLZjyYU/yU4nM867iFsGDWVQ+w6M6Xkob545nvMPr7hC0dQum1dt\nZceGzIjJV2+hl0/+V3m3yyFj+/P0nH/x/+3dd3hUZfbA8e+ZngqhQ4IUQQTpIAYQEBBBFBEFxIJg\nxF4QdVf8qVhQF0FXFNQVFcvKLrq6KlbsjbJSREVBOhgINYSWZDLl/f2RgISZFEi5k8z5PE+eMHdm\n7j3JE8689y3n7T2yOy07N+eMwZ1xeYr/oI9JiCk28UP+qtuAL3SA2Of188PH+auQj1417M328vpD\n/8GbE742f0mufvQy3LGFV+66Y91cNXlUoYVoz972Mnu3Z5F7ML8bM/eQl6xd+3n6phdP6Lqq6oi6\nln+Xhslc0/l0nl/2AzYRbCIYA8+dNzRsaz7O5WJc566M61zizCllgQOZB4tcAJW1a/8JnfOULidz\n778nAJCxcQfXtr+jyNe6YlwMvqZ/ieds36dN2EUjMfEeDu49FHbmkNhsbFu/g2Ztj7/iZZvurZgy\n/z5enPg6G37aTJ3kWoy+fyTdBndi744satargYiw+P1lIYvXgoEgSz5ZgTFGxwOqsahL/gC3pfZg\nRJu2fLN5I7FOJ/2bnUyCW/s4q6IWnZsTCDON0eVx0vM46tMX5fG0Z8kLMwZgd9hxOO107NeWS+8e\nVuJ5goEgwWM2/LE77JzS9WTsDhuZGaEVQf15fmo1qHnCsbfteSrTv3sYyL/zmDp2JlPHzkQE6jau\nw19m34jNHv7mv6jjqvqIyuQPkJyYyGXtOlgdhiojT6yb6/8+hn/c/gp5OXkYk98ar9MoiSE3DizT\nub05Xn5dsJpgmCJuDpeDGYsfLVWrPBgMct+QKfiPmUIqNhhyw0Bq1Eng14W/F5qd4/I46X5BV2rU\nKfvKcWMMfx3wEJt//ePIRi7b1m3n7nMf4fRzO7L4/WWFFrHZnXZ6XthNW/3VnH68qyrv/GsH8Nin\nk+g9ojvterdmzIOX8NzyaUVumF5axSU/l8dZ6u6Ytcs3hszyAfDnBfhk9pd07NuWCbOuJ7FOAu5Y\nF063k97Du/OXl4+/+mg4a5ZtYOvajJAdvPx5AZLq1yC5ZUNi4j04XA5iEjw0bFaPW2ZeXS7XVpEr\nalv+qno5rUcrTutRvltmujwuOpzVlhVfrSzUJ+90O+h76ZmlPo8vN6/I9QOHy0/0v6wXZ13Sgz1b\nM4lPii/X8hA7N+8K243j9/nZtWUPs356nGWf/cyW39JpfGoyXc5pX+7VSVXkidrkv/3gAWb/uJzl\n27fRIqkW4zp3pUWt2laHpSLMHS/dwPie93BoXzZ5OXm4PC4aNq9P2iOXlvocnoQYfN7QtSXuWHeh\nDxG73V4h0ytbdGoWtjaRO8ZFu95tsNlsnD6wY4UsNlORq1om/8PF6oq6bd+YtZdhc+eQ4/fjCwb4\naXsG769ZzUsXXERqSvFT9lR0qde4Dq+tm8mieUvJ2LCT5u1Poss5HbDZ8lvSwWAQESnyb+3lSXN5\n64n3Q2bUeOI9nNy+CQOv6lvhP0PD5vU586JUFrz7vyPjCnaHndgasQweV/JMpWPlZnvJzNhLrYZJ\nuhisCitTVU8RqQW8ATQFNgEjjTEh0xZEJAD8UvBwizGmxKWXJ1LVc29ODg988yWfrFtD0Bh6ndSU\nyX3PDim3fN0H7/LFxg0hm7Y0rVmTL0an6UCXKtG29dt56oYXWPHVSmx2G72Hd+fmGWkkJP25m9fa\n5RuY0Os+vMfMFrLZbUyYdR0DRvcpc53+0goEArzz1EfMe3Y+OQdzSB3SlbEPjaJ2w6RSnyMYDPLy\nvf/mnac+OrKb2bDxg7nq4UuPfBgq61VWVc+JwBfGmCkiMrHgcbiqTDnGmAq9pwwawyVvzWXzvix8\nBVPqvtuyiWFvzOHrseMKbbKyOP2PsLt1pe/fz4G8PBJ12qcqxsGsQ9yS+n8c2HsQEzQEA0G+fWsR\nm37dwj+WTzvSePj6zYVhu3tcHicmaCot8UN+l9Lw24cw/PYhJ3yO/zwxj3ee/rjQh9k7T39MQq14\nRt55YiuRlXXK+nE9FHi14N+vAheW8Xwn7Pstm8k4eOBI4gcIGEO2z8f7a1YXem1Rc/ptIqXeyUtF\nr8/++Q3enMIrcv15fjLW7+CX78p/N6+DWYfYtn572PUM5c2X52PJ/BUsnLeEQ/uzCz33n2nzQvZH\n8GZ7eXPavAqPS5W/srb86xtjMgCMMRkiUq+I13lEZCngB6YYY94N9yIRuRa4FuCkk45vVeOGvZn4\nwqySzPb7WLNnd6FjYzt05snFCwqVa3bZ7ZzXspVuvq7C8uX5+GLO93z9xgLSf98WtmJmMGj4Y/XW\nI7X8zxrZg/dmfBzS7RMMBEkdUvKK8ZyDOUxLe5bF7y/F7rDjdDu56ek0+l/Wq3x+qGOsXLCa+4ZM\nObIYze8LcOuz4xg4Jn9cYn/mwbDv27/nQIXEoypWiZlORD4HGoR56p7juM5JxphtItIc+FJEfjHG\nrD/2RcaYWcAsyO/zP47z06JWbZx2G3nBwq2jWIeT1nUKz6BI69SFDXsz+e/q33DbHfiCAc5ITmFy\n37OP55IqSgT8Af569kOs+3FjyIbwRxOBJkeVc27ZuTnD7xzCfx5/n4A/gM1mQwRuffYakurVKPG6\nj17+NMs+/Qmf14/P6yf3kJcnr32euim1y32zmNxsL/ec9yjZ+wuXMZ9x44u0ST2Fxq2SadImhU0r\n/wh5b1ElrFVkKzH5G2OKzIgiskNEGha0+hsCO4s4x7aC7xtE5GugExCS/MuiR+OTSEmskX8HUNBy\nsYuQ4HZxXsvC879tIjza/xwmpPZkbeYeUhIT2bJvH8PemMO6zD0kxcRwXZfTGdepqw7+RomsXfvY\nnZ5JoxYNQubYf//OD6z7cVOxid/pdnBSm5SQtQZjHxxF31FnsmjeUpwuB72Gp1KvccmbAO3J2FuQ\n+AuPGXizvcx97N0TTv47t+ziqRtfYNXiNSTWSSTt4UvpPbw7P3y0PGz1Ur8vwPyXv2LclCu44cmr\nmDR0SqG7HnesixueHHtCsShrlbWPYx4wBphS8P29Y18gIklAtjHGKyJ1gJ7A1DJeN4RNhDeGX8LD\n337Nh2t/J2AMfZs24/4+/Yosv1w3Lo66cXEsy9jKtR+8e6Ruf2ZODtMXL+RgXh4TUnuWd6gqguTl\n5jHtqmdZ8O4PON0OAr4Aw+8cwpgHLjnywb/4g6XkHgrdvMdmtyEiuDxO+l/ei2umjg7bWGjSOoUm\nrVOOK66927Nwuh1hB4x3FFGrvyRb12aQ1ua2IwvWDmQeYvLIvzPizgs46dTkkNpDkH/Xc3h1cuf+\n7Zj6+f289sCbbP71D5qc1pgrHxhJm9RTTigeZa2yJv8pwJsicjWwBRgBICJdgeuNMeOA1sDzIhIk\nf4B5ijEmtNB4OUh0e5g6YBBTBww6rvc9uWhhyIYtOX4/Ly5fxo1dz9BxgGrsmVtns3DeEnxe35FE\n+/bfP6BBk3oMSusHQGLtBOwOW+hc/Tg3/zdnPGec16Xc40o+pWHI9SC/7k6Hs8K3+gP+AEs//Ynd\n6Xs49YyWnNyhaaHnH73iqbDVQ996Yh6zV4V/zhPvocfQbkcet0k9hSmf3HucP42KRGWa7WOM2WOM\n6W+MaVnwPbPg+NKCxI8xZqExpp0xpkPB95fKI/DytC5zT9jjAuw8FFqTRVUPebl5fP76tyFVO3MP\neXlj6p9zEgaP6x92r12H00HnARWzt0NMnIfRk4bjiftzZprNbiMmzsOou0In1W3ftJMrmt/Eo5dO\n57nbX2F8z3uYdOFj+H1/Nmo2rNgU9lrGwOZVWxnxlwtwx7o5fPPiiXPToU8bug7UAojVkTZpyR8s\n3pkdmuQNhnpxcRZEpCpDzsFcilrkePReAE3aNOa2Wdcx/bpZ2B02jDHExHt45MP/w+mquB3dLvnr\nhTRsXp83pr7H3u1ZdOzXltH3jwhbAuLhUU+SuS2zUAXS5Z//zLszPj4yt9/hcoQUdzusdqMkeg4d\nRad+7fhk9pd4s72cdUlPeg7rpgu4qilN/sCE7j1Y/s62Ql0/MQ4HV3fqql0+1Vhi7QRq1K3B7vTC\nd34i+bXwj3b25b3peWE3flv4O+5YN61TW1ZK8bPew7vTe3jxe/lmbt/Lhp82h5Se9mbn8eELnx9J\n/gOv6sd7Mz8OeX9MgodTT28BQIc+p9Ghz2nlFL2KZPqRTv7uXrPOv5BTatVGgFoxMdyW2oMJqT2s\nDk1VIBHhlplX445xHenqsNlteOI9jJtyecjrY+I8dBnQgbY9T42oqpf+PD9FTUo7esD4xuljadWt\nRaHnnR4n07+bXJHhqQhVpto+FelEavsodSxfng+f119sieTfFq/h34/+l63rttP6jJZcds9FJLdo\neNzX2rd7Px/O+ozfFq2hadvGXHDjoFJN6ywrYwxjWt5CxoYdhY473Q4uuu08xv3tikLHN67cwndv\nLaJxq2T6XNJDu3WqmdLW9tHkr6ql3GwvM295iS//9T3BQIAGzeoz4fnr6HBW6bo01i7fwIsT5/D7\n0nXUapDE5fdcTP/Li15Zm7FxBzd3m0juIS95uT4cLgdOl4PHv3qAU7qcXF4/VpFW/W8tdw14iIA/\nQF6uD0+8m7opdZix6BHiaui4VTTR5K+i2r1D/saPX/xCXu6f3R7uWDfP/PA3khrUZPvGnTRsXr9Q\nFc7DNvy8mfE97iH3qDo27lg3Yx4cyYg7whekffDix1n43g8h/e4tOjXjuWXlvqwlrL07spj/ytdk\nbNhB+95t6DU8FZe74gakVWTS5K+i1o7Nu0hrPb5Q4of8/vxGJ9dn55bd+TNf8vycM7YvN89IK9SH\nf/+wqSyat4Rj/2vExHt4a9fssAn1gsTR5BwMvxBs3v7XcMdopVhVOUqb/LWzT1U72zfuxBkmQQcD\nQbau3U5ero/s/Tnk5fr44PlPuajWWJ6d8DIH9uYXLluzdH1I4of8vvU9WzPDXtNdxKYmNrutUks3\nK1VamvxVtXNSm5SQVv9hIXe6BrIP5PLBc59yc7eJeHO8NGheP+x7g4EgNeslhn3u/OsG4IpxFTrm\ndDnodXFq2AViSllNk7+qULvS9zDn0beZeetsFr2/lECg4mvSJ9WrwcCxZxVqjZdUoM+X5ydz+z6+\nfmMho+8bjju2cCJ3x7g4Z2xfYuLDzxq67J6LOH1QR1weJ7GJMbhj3ZzS9WTGP3dN2X8gpSqA9vmr\nCrNk/goevPhxgoEAPq8fT7yHlp2a8dhn91XoyljI33Lwnac+5O3pH3JoXzYd+pzG1nUZbFm1tdj3\nDUrrxx0v3sDXby7gudteYX/mQewOG+ddczbXTB1dYis+fW0GG3/eTKMWDUJq61S29DXbePm+uaz8\nbhW1Gtbk0rsvKnHBmKr6dMBXWcrv8zOywTVH+tEPc8e6ue7xKxly/TmVHtPKBauZOPBh8nLywpZ1\ncHmcjJ40glEThwH5XUQHMg8Sk+Cp8A+r8rZt/XZu6PLX/BIWBTOQxCY0b9+E21+4vlKmnypr6ICv\nstTa5RvDbjvozfbyxevfWhBRfsmGGYseofeIVOzO0EFYu8POwKv6HnksIiTWTqhyiR/g9clvkXuo\n8FaTJmhYv2ITE3pP4ps3F1oYnYoEmvxVhXA47UUWTXN6rEumzdo14d65t/PP9c/Qtlfr/MVYbieN\nWzVi2hf3k1S/pmWxladfF6wOW6IZIC8nj+k3zKqUPYFV5NJpCKpCnNyxKXE140Lmvnvi3Jx3jfXb\nZdZNqc2T3zzE/swD+Lx+ajdMCvu6QCBA7iEvsQkxVWpXt/pN67Ft/Y4inw/4AqSv2UaTNroFY7TS\nlr+qEDabjcnv3UV8UhwxCR7cMS5cMS76jOhOn5GRUzAvsVZC2MQfCAR46e45XFhzDBfXSeOyJtfz\n7VuLLIjwxFx697CQGUtH8/sDxIdZ3ayih7b8VYVp0akZc9OfZ/EHy9m/ez/t+7SpMi3N5+98jY9e\n+PzIfrW70zOZOnYm8UnxdO7fzuLoStapXztue/46nr7xBXIOFL77cjjttD6jZZF3Oyo66GwfpY6R\nm+1leN00vMfs8AXQ9sxTefLbqlMC2e8P8I87XuWjWZ/h8rjw+wI0PS2Fhz+4m5p1a1gdnqoApZ3t\noy1/pY6RtXMfYgvfv19cP3okcjjs3PxUGlfeP4J1P26idqOk495MXlVPmvyVOkbtRklImBr3ItCi\nY9PKD6gcJNZKqBLdVary6ICvUsdwupxcfu/FeI4p1uaKcTF28iiLolKqfGnLX6kwRt55AUn1avCv\nR94mc3sWLTo145qpo2nZubnVoSlVLnTAN4xsnw+v309Nj6dKze1WSikd8D0B+7253PX5fL7cuAGA\n5IREppw9kG7JOkCmlKpetM//KGnvvcOXGzfgCwbxBYNs2pfFVe+9zaasvVaHppRS5UqTf4Hf9+xm\n1e6d+IKF66H4AgFeXfGjRVEppVTF0ORfIH3fPhxhpvf5jWF9Vvit+5RSqqrS5F/g1Lp1yQuzy5Tb\nbqdrw2QLIooOwWCQYDB89UmlVMUpU/IXkREi8quIBEWkyNFlERkkIr+LyDoRmViWa1aU5IREzmvZ\nihjHn2PgNhHiXC6uaN/Bwsiqp707snjg4mkM9lzGINco0lqP59UH3mBX+h6rQ1MqKpRpqqeItAaC\nwPPAncaYkLmZImIH1gADgHRgCXCpMea34s5txVTPQDDIyyuW89rPP3IoL48+TZpxR48zSU4Iv2m3\nOjEBf4C01uPZsXl3SE15p8fJLTOv5ty0/hZFp1TVVilTPY0xqwouVtzLugHrjDEbCl47FxgKFJv8\nrWC32RjXuSvjOpf4e1Nl8MPHP7J3576wm4n4cn3MvPklup3bWatOKlWBKqPPPxn446jH6QXHQojI\ntSKyVESW7tq1qxJCU1ZIX5OBL9dX5PMiwsL3llRiREpFnxKTv4h8LiIrw3wNLeU1wt0WhO1rMsbM\nMsZ0NcZ0rVu3bilPr6qapqel4HQXv5Xj0XvPKqXKX4ndPsaYsu65lw4cvYNHCrCtjOdUVViXczpQ\nr0ld0n/fFrbrxxhDj6Ha9aZURaqMbp8lQEsRaSYiLmAUMK8SrqsilM1mY/p3kxlwZW8cTnv+QQG7\n047L4+S6J8ZQJ7m2tUEqVc2VdbbPMGAGUBfIAlYYYwaKSCPgRWPM4ILXDQamA3ZgtjHmkZLOrTt5\nRY/Nv/3BgneXYHfY6T08lYbN61sdklJVVmln+2hVT6WUqkZKm/x1ha9SSkUhTf5KKRWFNPkrpVQU\n0uSvlFJRSJO/UkpFId3GUalyln0gh9cnv8UXc74DoP/lvRg9aTgx8TEWR6bUnzT5K1WOAoEAt/eZ\nxJZVW/F58+sXvTvjY3784heeWTIFW5gNg5Sygv4lKlWOln6ygm3rth9J/AA+r4+tazNYOv8nCyNT\nqjBN/kqVo7XLN5JzKDfkeG62l7XLN1gQkVLhafJXqhzVb1qXmDhPyHFPrJsGTetZEJFS4WnyVxEv\na9c+5j72Lo9dOYP3nv2E7AM5VodUpF4Xp+KKcRXa4EhsgjvGxZkXdbMwMqUK09o+KqJt/GUzE3pP\nwuf1kZfrwxPrJrZGLM8smUKdRrWsDi+s9LUZPHblDNYVdPO07Nycu/55C8ktGlocmYoGWthNVQs3\ndZvImqXrCx2zO2z0uaQnd//zVouiKp0Dew8CkJAUb3EkKppUyh6+SlWk3Gwv61dsDDke8AdZ/P4y\nCyI6Ppr0VSTTPn8VsWx2W6G+86O53NpuUaosNPmriOVyO+k2uPOfu30dPu5xMujqfhZFpVT1oMlf\nRbTbX7ielFaNiIn34Ilz4451c1rPUxk9aYTVoSlVpem9s4poNeokMuunJ1j5/Wq2rd9O8/ZNaNm5\nudVhKVXlafJXEU9EaNerNe16tbY6FKWqDe32UUqpKKTJXymlopAmf6WUikKa/JVSKgpp8ldKqSik\nyV8ppaJQxBZ2E5FdwOZKuFQdYHclXKc8aKwVpyrFq7FWjKoUKxQdbxNjTN2S3hyxyb+yiMjS0lTA\niwQaa8WpSvFqrBWjKsUKZY9Xu32UUioKafJXSqkopMkfZlkdwHHQWCtOVYpXY60YVSlWKGO8Ud/n\nr5RS0Uhb/kopFYU0+SulVBTS5A+IyGQR+VlEVojIpyLSyOqYiiIi00RkdUG874hITatjKoqIjBCR\nX0UkKCIROYVORAaJyO8isk5EJlodT3FEZLaI7BSRlVbHUhIRaSwiX4nIqoK/gfFWx1QUEfGIyA8i\n8lNBrA9aHVNJRMQuIj+KyAcneg5N/vmmGWPaG2M6Ah8Ak6wOqBifAW2NMe2BNcDdFsdTnJXARcC3\nVgcSjojYgWeAc4E2wKUi0sbaqIr1CjDI6iBKyQ/cYYxpDaQCN0Xw79YL9DPGdAA6AoNEJNXimEoy\nHlhVlhNo8geMMfuPehgHROwouDHmU2OMv+DhYiDFyniKY4xZZYz53eo4itENWGeM2WCMyQPmAkMt\njqlIxphvgUyr4ygNY0yGMWZ5wb8PkJ+okq2NKjyT72DBQ2fBV8TmABFJAc4DXizLeTT5FxCRR0Tk\nD+ByIrvlf7Q04GOrg6jCkoE/jnqcToQmqKpMRJoCnYD/WRtJ0Qq6UVYAO4HPjDERGyswHfgrECzL\nSaIm+YvI5yKyMszXUABjzD3GmMbAHODmSI614DX3kH9rPce6SEsXawSTMMcitsVXFYlIPPA2cNsx\nd9gRxRgTKOj2TQG6iUhbq2MKR0TOB3YaY5aV9VxRs4evMebsUr70X8CHwP0VGE6xSopVRMYA5wP9\njcULNY7j9xqJ0oHGRz1OAbZZFEu1IyJO8hP/HGPMf62OpzSMMVki8jX5YyuROLDeE7hARAYDHiBR\nRF43xlxxvCeKmpZ/cUSk5VEPLwBWWxVLSURkEHAXcIExJtvqeKq4JUBLEWkmIi5gFDDP4piqBRER\n4CVglTHm71bHUxwRqXt41pyIxABnE6E5wBhztzEmxRjTlPy/1y9PJPGDJv/DphR0VfwMnEP+SHqk\nmgkkAJ8VTE39h9UBFUVEholIOtAd+FBE5lsd09EKBs5vBuaTPyD5pjHmV2ujKpqI/BtYBLQSkXQR\nudrqmIrRExgN9Cv4O11R0FqNRA2Brwr+/y8hv8//hKdQVhVa3kEppaKQtvyVUioKafJXSqkopMlf\nKaWikCZ/pZSKQpr8lVIqCmnyV0qpKKTJXymlotD/A/FZIbSliTn2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa1e87e1eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2d_df[2] = ypred_agg\n",
    "plt.scatter(data2d_df[0], data2d_df[1], c=data2d_df[2])\n",
    "plt.title(\"Plot da clusterização Agglomerative\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eqtLHzP6d0K16bdy1lMPMBgP/DvyoN/ejcC9gZgMIwX6tu98cdz1dcAAw18xe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\nCncRkQT6/7IzJohM7vAHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa1e82914a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#metodo do cotovelo para escolha de K\n",
    "distortions = []\n",
    "K = range(1,15)\n",
    "for k in K:\n",
    "    kmeanModel = KMeans(n_clusters=k).fit(X)\n",
    "    kmeanModel.fit(X)\n",
    "    distortions.append(sum(np.min(cdist(X, kmeanModel.cluster_centers_, 'euclidean'), axis=1)) / X.shape[0])\n",
    "\n",
    "plt.plot(K, distortions, 'bx-')\n",
    "plt.xlabel('K')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metricas homogeneity, completeness e v measure respectivamente para o KMeans, k=4 \n",
      " (0.80831384236370951, 0.65221133555143129, 0.72192038678209602)\n"
     ]
    },
    {
     "data": {
      "image/png": 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L5v8acGol508DekdeE4H/1lO/Go1Go6kF9aL8lVKzgdxKipwDTFE284AMEWlfH31rNBqN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\nhnC8UMhuMNqD51Twz4mYeKrhuJF8hV0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R7EY/NtesWrKWufe/ETdayOF0cNWff8XQ8QNL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\nPesyzp44OEnRGVO71chQT1VdEb1YWYf1Blar6trosS8Ao4A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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa1e82d6f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Pelo grafico o melhor K seria K=4 logo temos o plot e metricas para esse K\n",
    "kmeans = KMeans(n_clusters=4, random_state=0).fit(X)\n",
    "ypred_kmeans = kmeans.predict(X)\n",
    "metrics_kmeans = metrics.homogeneity_completeness_v_measure(y, ypred_kmeans)\n",
    "print(\"Metricas homogeneity, completeness e v measure respectivamente para o KMeans, k=4 \\n\", metrics_kmeans)\n",
    "data2d = PCA(n_components=2).fit_transform(X)\n",
    "data2d_df = pd.DataFrame(data2d)\n",
    "data2d_df[2] = ypred_kmeans\n",
    "plt.scatter(data2d_df[0], data2d_df[1], c=data2d_df[2])\n",
    "plt.title(\"Plot da clusterização K-mean, K=4\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Usando K=4 vemos que a metrica de homogenidade melhorou e a v-measure ficou quase igual. No entanto a metrica de completude ficou um pouco abaixo para o valor inicial de K=3"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
